Bin Wang


2022

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Offline-to-Online Co-Evolutional User Simulator and Dialogue System
Dafeng Chi | Yuzheng Zhuang | Yao Mu | Bin Wang | Jianzhu Bao | Yasheng Wang | Yuhan Dong | Xin Jiang | Qun Liu | Jianye Hao
Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD)

Reinforcement learning (RL) has emerged as a promising approach to fine-tune offline pretrained GPT-2 model in task-oriented dialogue (TOD) systems. In order to obtain human-like online interactions while extending the usage of RL, building pretrained user simulators (US) along with dialogue systems (DS) and facilitating jointly fine-tuning via RL becomes prevalent. However, joint training brings distributional shift problem caused by compounding exposure bias. Existing methods usually iterative update US and DS to ameliorate the ensued non-stationarity problem, which could lead to sub-optimal policy and less sample efficiency. To take a step further for tackling the problem, we introduce an Offline-to-oNline Co-Evolutional (ONCE) framework, which enables bias-aware concurrent joint update for RL-based fine-tuning whilst takes advantages from GPT-2 based end-to-end modeling on US and DS. Extensive experiments demonstrate that ONCE builds high-quality loops of policy learning and dialogues data collection, and achieves state-of-the-art online and offline evaluation results on MultiWOZ2.1 dataset. Opensourced code will be implemented with Mindspore (MS, 2022) and released on our homepage.

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BIT-Xiaomi’s System for AutoSimTrans 2022
Mengge Liu | Xiang Li | Bao Chen | Yanzhi Tian | Tianwei Lan | Silin Li | Yuhang Guo | Jian Luan | Bin Wang
Proceedings of the Third Workshop on Automatic Simultaneous Translation

This system paper describes the BIT-Xiaomi simultaneous translation system for Autosimtrans 2022 simultaneous translation challenge. We participated in three tracks: the Zh-En text-to-text track, the Zh-En audio-to-text track and the En-Es test-to-text track. In our system, wait-k is employed to train prefix-to-prefix translation models. We integrate streaming chunking to detect boundaries as the source streaming read in. We further improve our system with data selection, data-augmentation and R-drop training methods. Results show that our wait-k implementation outperforms organizer’s baseline by 8 BLEU score at most, and our proposed streaming chunking method further improves about 2 BLEU in low latency regime.

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MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation
Quan Tu | Yanran Li | Jianwei Cui | Bin Wang | Ji-Rong Wen | Rui Yan
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Applying existing methods to emotional support conversation—which provides valuable assistance to people who are in need—has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user’s instant mental state; (b) most of them focus on expressing empathy in the response(s) rather than gradually reducing user’s distress. To address the problems, we propose a novel model MISC, which firstly infers the user’s fine-grained emotional status, and then responds skillfully using a mixture of strategy. Experimental results on the benchmark dataset demonstrate the effectiveness of our method and reveal the benefits of fine-grained emotion understanding as well as mixed-up strategy modeling.

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Just Rank: Rethinking Evaluation with Word and Sentence Similarities
Bin Wang | C.-C. Jay Kuo | Haizhou Li
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Word and sentence embeddings are useful feature representations in natural language processing. However, intrinsic evaluation for embeddings lags far behind, and there has been no significant update since the past decade. Word and sentence similarity tasks have become the de facto evaluation method. It leads models to overfit to such evaluations, negatively impacting embedding models’ development. This paper first points out the problems using semantic similarity as the gold standard for word and sentence embedding evaluations. Further, we propose a new intrinsic evaluation method called EvalRank, which shows a much stronger correlation with downstream tasks. Extensive experiments are conducted based on 60+ models and popular datasets to certify our judgments. Finally, the practical evaluation toolkit is released for future benchmarking purposes.

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C3KG: A Chinese Commonsense Conversation Knowledge Graph
Dawei Li | Yanran Li | Jiayi Zhang | Ke Li | Chen Wei | Jianwei Cui | Bin Wang
Findings of the Association for Computational Linguistics: ACL 2022

Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational models to plan the next steps. To fill the gap, we curate a large-scale multi-turn human-written conversation corpus, and create the first Chinese commonsense conversation knowledge graph which incorporates both social commonsense knowledge and dialog flow information. To show the potential of our graph, we develop a graph-conversation matching approach, and benchmark two graph-grounded conversational tasks. All the resources in this work will be released to foster future research.

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Analyzing and Evaluating Faithfulness in Dialogue Summarization
Bin Wang | Chen Zhang | Yan Zhang | Yiming Chen | Haizhou Li
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Dialogue summarization is abstractive in nature, making it suffer from factual errors. The factual correctness of summaries has the highest priority before practical applications. Many efforts have been made to improve faithfulness in text summarization. However, there is a lack of systematic study on dialogue summarization systems. In this work, we first perform the fine-grained human analysis on the faithfulness of dialogue summaries and observe that over 35% of generated summaries are faithfully inconsistent respective the source dialogues. Furthermore, we present a new model-level faithfulness evaluation method. It examines generation models with multi-choice questions created by rule-based transformations. Experimental results show that our evaluation schema is a strong proxy for the factual correctness of summarization models. The human-annotated faithfulness samples and the evaluation toolkit are released to facilitate future research toward faithful dialogue summarization.

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Structure-Unified M-Tree Coding Solver for Math Word Problem
Bin Wang | Jiangzhou Ju | Yang Fan | Xinyu Dai | Shujian Huang | Jiajun Chen
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

As one of the challenging NLP tasks, designing math word problem (MWP) solvers has attracted increasing research attention for the past few years. In previous work, models designed by taking into account the properties of the binary tree structure of mathematical expressions at the output side have achieved better performance. However, the expressions corresponding to a MWP are often diverse (e.g., n1+n2 × n3-n4, n3× n2-n4+n1, etc.), and so are the corresponding binary trees, which creates difficulties in model learning due to the non-deterministic output space. In this paper, we propose the Structure-Unified M-Tree Coding Solver (SUMC-Solver), which applies a tree with any M branches (M-tree) to unify the output structures. To learn the M-tree, we use a mapping to convert the M-tree into the M-tree codes, where codes store the information of the paths from tree root to leaf nodes and the information of leaf nodes themselves, and then devise a Sequence-to-Code (seq2code) model to generate the codes. Experimental results on the widely used MAWPS and Math23K datasets have demonstrated that SUMC-Solver not only outperforms several state-of-the-art models under similar experimental settings but also performs much better under low-resource conditions.

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Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework
Yiming Chen | Yan Zhang | Bin Wang | Zuozhu Liu | Haizhou Li
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Most sentence embedding techniques heavily rely on expensive human-annotated sentence pairs as the supervised signals. Despite the use of large-scale unlabeled data, the performance of unsupervised methods typically lags far behind that of the supervised counterparts in most downstream tasks. In this work, we propose a semi-supervised sentence embedding framework, GenSE, that effectively leverages large-scale unlabeled data. Our method include three parts: 1) Generate: A generator/discriminator model is jointly trained to synthesize sentence pairs from open-domain unlabeled corpus; 2) Discriminate: Noisy sentence pairs are filtered out by the discriminator to acquire high-quality positive and negative sentence pairs; 3) Contrast: A prompt-based contrastive approach is presented for sentence representation learning with both annotated and synthesized data. Comprehensive experiments show that GenSE achieves an average correlation score of 85.19 on the STS datasets and consistent performance improvement on four domain adaptation tasks, significantly surpassing the state-of-the-art methods and convincingly corroborating its effectiveness and generalization ability.

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The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 2022
Bao Guo | Mengge Liu | Wen Zhang | Hexuan Chen | Chang Mu | Xiang Li | Jianwei Cui | Bin Wang | Yuhang Guo
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

This system paper describes the Xiaomi Translation System for the IWSLT 2022 Simultaneous Speech Translation (noted as SST) shared task. We participate in the English-to-Mandarin Chinese Text-to-Text (noted as T2T) track. Our system is built based on the Transformer model with novel techniques borrowed from our recent research work. For the data filtering, language-model-based and rule-based methods are conducted to filter the data to obtain high-quality bilingual parallel corpora. We also strengthen our system with some dominating techniques related to data augmentation, such as knowledge distillation, tagged back-translation, and iterative back-translation. We also incorporate novel training techniques such as R-drop, deep model, and large batch training which have been shown to be beneficial to the naive Transformer model. In the SST scenario, several variations of extttwait-k strategies are explored. Furthermore, in terms of robustness, both data-based and model-based ways are used to reduce the sensitivity of our system to Automatic Speech Recognition (ASR) outputs. We finally design some inference algorithms and use the adaptive-ensemble method based on multiple model variants to further improve the performance of the system. Compared with strong baselines, fusing all techniques can improve our system by 2 extasciitilde3 BLEU scores under different latency regimes.

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Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training
Zhongjian Miao | Xiang Li | Liyan Kang | Wen Zhang | Chulun Zhou | Yidong Chen | Bin Wang | Min Zhang | Jinsong Su
Proceedings of the 29th International Conference on Computational Linguistics

Most existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples. They require the model to translate both the authentic source sentence and its adversarial counterpart into the identical target sentence within the same training stage, which may be a suboptimal choice to achieve robust NMT. In this paper, we first conduct a preliminary study to confirm this claim and further propose an Iterative Scheduled Data-switch Training Framework to mitigate this problem. Specifically, we introduce two training stages, iteratively switching between authentic and adversarial examples. Compared with previous studies, our model focuses more on just one type of examples at each single stage, which can better exploit authentic and adversarial examples, and thus obtaining a better robust NMT model. Moreover, we introduce an improved curriculum learning method with a sampling strategy to better schedule the process of noise injection. Experimental results show that our model significantly surpasses several competitive baselines on four translation benchmarks. Our source code is available at https://github.com/DeepLearnXMU/RobustNMT-ISDST.

2021

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Maximal Clique Based Non-Autoregressive Open Information Extraction
Bowen Yu | Yucheng Wang | Tingwen Liu | Hongsong Zhu | Limin Sun | Bin Wang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Open Information Extraction (OpenIE) aims to discover textual facts from a given sentence. In essence, the facts contained in plain text are unordered. However, the popular OpenIE systems usually output facts sequentially in the way of predicting the next fact conditioned on the previous decoded ones, which enforce an unnecessary order on the facts and involve the error accumulation between autoregressive steps. To break this bottleneck, we propose MacroIE, a novel non-autoregressive framework for OpenIE. MacroIE firstly constructs a fact graph based on the table filling scheme, in which each node denotes a fact element, and an edge links two nodes that belong to the same fact. Then OpenIE can be reformulated as a non-parametric process of finding maximal cliques from the graph. It directly outputs the final set of facts in one go, thus getting rid of the burden of predicting fact order, as well as the error propagation between facts. Experiments conducted on two benchmark datasets show that our proposed model significantly outperforms current state-of-the-art methods, beats the previous systems by as much as 5.7 absolute gain in F1 score.

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Few-Shot Event Detection with Prototypical Amortized Conditional Random Field
Xin Cong | Shiyao Cui | Bowen Yu | Tingwen Liu | Wang Yubin | Bin Wang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Pre-training with Meta Learning for Chinese Word Segmentation
Zhen Ke | Liang Shi | Songtao Sun | Erli Meng | Bin Wang | Xipeng Qiu
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Recent researches show that pre-trained models (PTMs) are beneficial to Chinese Word Segmentation (CWS). However, PTMs used in previous works usually adopt language modeling as pre-training tasks, lacking task-specific prior segmentation knowledge and ignoring the discrepancy between pre-training tasks and downstream CWS tasks. In this paper, we propose a CWS-specific pre-trained model MetaSeg, which employs a unified architecture and incorporates meta learning algorithm into a multi-criteria pre-training task. Empirical results show that MetaSeg could utilize common prior segmentation knowledge from different existing criteria and alleviate the discrepancy between pre-trained models and downstream CWS tasks. Besides, MetaSeg can achieve new state-of-the-art performance on twelve widely-used CWS datasets and significantly improve model performance in low-resource settings.

2020

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Focus-Constrained Attention Mechanism for CVAE-based Response Generation
Zhi Cui | Yanran Li | Jiayi Zhang | Jianwei Cui | Chen Wei | Bin Wang
Findings of the Association for Computational Linguistics: EMNLP 2020

To model diverse responses for a given post, one promising way is to introduce a latent variable into Seq2Seq models. The latent variable is supposed to capture the discourse-level information and encourage the informativeness of target responses. However, such discourse-level information is often too coarse for the decoder to be utilized. To tackle it, our idea is to transform the coarse-grained discourse-level information into fine-grained word-level information. Specifically, we firstly measure the semantic concentration of corresponding target response on the post words by introducing a fine-grained focus signal. Then, we propose a focus-constrained attention mechanism to take full advantage of focus in well aligning the input to the target response. The experimental results demonstrate that by exploiting the fine-grained signal, our model can generate more diverse and informative responses compared with several state-of-the-art models.

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Xiaomi’s Submissions for IWSLT 2020 Open Domain Translation Task
Yuhui Sun | Mengxue Guo | Xiang Li | Jianwei Cui | Bin Wang
Proceedings of the 17th International Conference on Spoken Language Translation

This paper describes the Xiaomi’s submissions to the IWSLT20 shared open domain translation task for Chinese<->Japanese language pair. We explore different model ensembling strategies based on recent Transformer variants. We also further strengthen our systems via some effective techniques, such as data filtering, data selection, tagged back translation, domain adaptation, knowledge distillation, and re-ranking. Our resulting Chinese->Japanese primary system ranked second in terms of character-level BLEU score among all submissions. Our resulting Japanese->Chinese primary system also achieved a competitive performance.

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Lijunyi at SemEval-2020 Task 4: An ALBERT Model Based Maximum Ensemble with Different Training Sizes and Depths for Commonsense Validation and Explanation
Junyi Li | Bin Wang | Haiyan Ding
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This article describes the system submitted to SemEval 2020 Task 4: Commonsense Validation and Explanation. We only participated in the subtask A, which is mainly to distinguish whether the sentence has meaning. To solve this task, we mainly used ALBERT model-based maximum ensemble with different training sizes and depths. To prove the validity of the model to the task, we also used some other neural network models for comparison. Our model achieved the accuracy score of 0.938(ranked 10/41) in subtask A.

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Lee at SemEval-2020 Task 5: ALBERT Model Based on the Maximum Ensemble Strategy and Different Data Sampling Methods for Detecting Counterfactual Statements
Junyi Li | Yuhang Wu | Bin Wang | Haiyan Ding
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This article describes the system submitted to SemEval 2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals. In this task, we only participate in the subtask A which is detecting counterfactual statements. In order to solve this sub-task, first of all, because of the problem of data balance, we use the undersampling and oversampling methods to process the data set. Second, we used the ALBERT model and the maximum ensemble method based on the ALBERT model. Our methods achieved a F1 score of 0.85 in subtask A.

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Infusing Sequential Information into Conditional Masked Translation Model with Self-Review Mechanism
Pan Xie | Zhi Cui | Xiuying Chen | XiaoHui Hu | Jianwei Cui | Bin Wang
Proceedings of the 28th International Conference on Computational Linguistics

Non-autoregressive models generate target words in a parallel way, which achieve a faster decoding speed but at the sacrifice of translation accuracy. To remedy a flawed translation by non-autoregressive models, a promising approach is to train a conditional masked translation model (CMTM), and refine the generated results within several iterations. Unfortunately, such approach hardly considers the sequential dependency among target words, which inevitably results in a translation degradation. Hence, instead of solely training a Transformer-based CMTM, we propose a Self-Review Mechanism to infuse sequential information into it. Concretely, we insert a left-to-right mask to the same decoder of CMTM, and then induce it to autoregressively review whether each generated word from CMTM is supposed to be replaced or kept. The experimental results (WMT14 En ↔ De and WMT16 En ↔ Ro) demonstrate that our model uses dramatically less training computations than the typical CMTM, as well as outperforms several state-of-the-art non-autoregressive models by over 1 BLEU. Through knowledge distillation, our model even surpasses a typical left-to-right Transformer model, while significantly speeding up decoding.

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Porous Lattice Transformer Encoder for Chinese NER
Xue Mengge | Bowen Yu | Tingwen Liu | Yue Zhang | Erli Meng | Bin Wang
Proceedings of the 28th International Conference on Computational Linguistics

Incorporating lexicons into character-level Chinese NER by lattices is proven effective to exploitrich word boundary information. Previous work has extended RNNs to consume lattice inputsand achieved great success. However, due to the DAG structure and the inherently unidirectionalsequential nature, this method precludes batched computation and sufficient semantic interaction.In this paper, we propose PLTE, an extension of transformer encoder that is tailored for ChineseNER, which models all the characters and matched lexical words in parallel with batch process-ing. PLTE augments self-attention with positional relation representations to incorporate latticestructure. It also introduces a porous mechanism to augment localness modeling and maintainthe strength of capturing the rich long-term dependencies. Experimental results show that PLTEperforms up to 11.4 times faster than state-of-the-art methods while realizing better performance.We also demonstrate that using BERT representations further substantially boosts the performanceand brings out the best in PLTE.

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Learning to Prune Dependency Trees with Rethinking for Neural Relation Extraction
Bowen Yu | Xue Mengge | Zhenyu Zhang | Tingwen Liu | Wang Yubin | Bin Wang
Proceedings of the 28th International Conference on Computational Linguistics

Dependency trees have been shown to be effective in capturing long-range relations between target entities. Nevertheless, how to selectively emphasize target-relevant information and remove irrelevant content from the tree is still an open problem. Existing approaches employing pre-defined rules to eliminate noise may not always yield optimal results due to the complexity and variability of natural language. In this paper, we present a novel architecture named Dynamically Pruned Graph Convolutional Network (DP-GCN), which learns to prune the dependency tree with rethinking in an end-to-end scheme. In each layer of DP-GCN, we employ a selection module to concentrate on nodes expressing the target relation by a set of binary gates, and then augment the pruned tree with a pruned semantic graph to ensure the connectivity. After that, we introduce a rethinking mechanism to guide and refine the pruning operation by feeding back the high-level learned features repeatedly. Extensive experimental results demonstrate that our model achieves impressive results compared to strong competitors.

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Modeling Discourse Structure for Document-level Neural Machine Translation
Junxuan Chen | Xiang Li | Jiarui Zhang | Chulun Zhou | Jianwei Cui | Bin Wang | Jinsong Su
Proceedings of the First Workshop on Automatic Simultaneous Translation

Recently, document-level neural machine translation (NMT) has become a hot topic in the community of machine translation. Despite its success, most of existing studies ignored the discourse structure information of the input document to be translated, which has shown effective in other tasks. In this paper, we propose to improve document-level NMT with the aid of discourse structure information. Our encoder is based on a hierarchical attention network (HAN) (Miculicich et al., 2018). Specifically, we first parse the input document to obtain its discourse structure. Then, we introduce a Transformer-based path encoder to embed the discourse structure information of each word. Finally, we combine the discourse structure information with the word embedding before it is fed into the encoder. Experimental results on the English-to-German dataset show that our model can significantly outperform both Transformer and Transformer+HAN.

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Coarse-to-Fine Pre-training for Named Entity Recognition
Xue Mengge | Bowen Yu | Zhenyu Zhang | Tingwen Liu | Yue Zhang | Bin Wang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

More recently, Named Entity Recognition hasachieved great advances aided by pre-trainingapproaches such as BERT. However, currentpre-training techniques focus on building lan-guage modeling objectives to learn a gen-eral representation, ignoring the named entity-related knowledge. To this end, we proposea NER-specific pre-training framework to in-ject coarse-to-fine automatically mined entityknowledge into pre-trained models. Specifi-cally, we first warm-up the model via an en-tity span identification task by training it withWikipedia anchors, which can be deemed asgeneral-typed entities. Then we leverage thegazetteer-based distant supervision strategy totrain the model extract coarse-grained typedentities. Finally, we devise a self-supervisedauxiliary task to mine the fine-grained namedentity knowledge via clustering.Empiricalstudies on three public NER datasets demon-strate that our framework achieves significantimprovements against several pre-trained base-lines, establishing the new state-of-the-art per-formance on three benchmarks. Besides, weshow that our framework gains promising re-sults without using human-labeled trainingdata, demonstrating its effectiveness in label-few and low-resource scenarios.

2019

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Adaptive Convolution for Multi-Relational Learning
Xiaotian Jiang | Quan Wang | Bin Wang
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We consider the problem of learning distributed representations for entities and relations of multi-relational data so as to predict missing links therein. Convolutional neural networks have recently shown their superiority for this problem, bringing increased model expressiveness while remaining parameter efficient. Despite the success, previous convolution designs fail to model full interactions between input entities and relations, which potentially limits the performance of link prediction. In this work we introduce ConvR, an adaptive convolutional network designed to maximize entity-relation interactions in a convolutional fashion. ConvR adaptively constructs convolution filters from relation representations, and applies these filters across entity representations to generate convolutional features. As such, ConvR enables rich interactions between entity and relation representations at diverse regions, and all the convolutional features generated will be able to capture such interactions. We evaluate ConvR on multiple benchmark datasets. Experimental results show that: (1) ConvR performs substantially better than competitive baselines in almost all the metrics and on all the datasets; (2) Compared with state-of-the-art convolutional models, ConvR is not only more effective but also more efficient. It offers a 7% increase in MRR and a 6% increase in Hits@10, while saving 12% in parameter storage.

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YNU NLP at SemEval-2019 Task 5: Attention and Capsule Ensemble for Identifying Hate Speech
Bin Wang | Haiyan Ding
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes the system submitted to SemEval 2019 Task 5: Multilingual detection of hate speech against immigrants and women in Twitter (hatEval). Its main purpose is to conduct hate speech detection on Twitter, which mainly includes two specific different targets, immigrants and women. We participate in both subtask A and subtask B for English. In order to address this task, we develope an ensemble of an attention-LSTM model based on HAN and an BiGRU-capsule model. Both models use fastText pre-trained embeddings, and we use this model in both subtasks. In comparison to other participating teams, our system is ranked 16th in the Sub-task A for English, and 12th in the Sub-task B for English.

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YNUWB at SemEval-2019 Task 6: K-max pooling CNN with average meta-embedding for identifying offensive language
Bin Wang | Xiaobing Zhou | Xuejie Zhang
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes the system submitted to SemEval 2019 Task 6: OffensEval 2019. The task aims to identify and categorize offensive language in social media, we only participate in Sub-task A, which aims to identify offensive language. In order to address this task, we propose a system based on a K-max pooling convolutional neural network model, and use an argument for averaging as a valid meta-embedding technique to get a metaembedding. Finally, we also use a cyclic learning rate policy to improve model performance. Our model achieves a Macro F1-score of 0.802 (ranked 9/103) in the Sub-task A.

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YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction
Junyi Li | Xiaobing Zhou | Yuhang Wu | Bin Wang
Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

We participated in the BioNLP 2019 Open Shared Tasks: binary relation extraction of SeeDev task. The model was constructed us- ing convolutional neural networks (CNN) and long short term memory networks (LSTM). The full text information and context information were collected using the advantages of CNN and LSTM. The model consisted of two main modules: distributed semantic representation construction, such as word embedding, distance embedding and entity type embed- ding; and CNN-LSTM model. The F1 value of our participated task on the test data set of all types was 0.342. We achieved the second highest in the task. The results showed that our proposed method performed effectively in the binary relation extraction.

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An Improved Coarse-to-Fine Method for Solving Generation Tasks
Wenyv Guan | Qianying Liu | Guangzhi Han | Bin Wang | Sujian Li
Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association

The coarse-to-fine (coarse2fine) methods have recently been widely used in the generation tasks. The methods first generate a rough sketch in the coarse stage and then use the sketch to get the final result in the fine stage. However, they usually lack the correction ability when getting a wrong sketch. To solve this problem, in this paper, we propose an improved coarse2fine model with a control mechanism, with which our method can control the influence of the sketch on the final results in the fine stage. Even if the sketch is wrong, our model still has the opportunity to get a correct result. We have experimented our model on the tasks of semantic parsing and math word problem solving. The results have shown the effectiveness of our proposed model.

2018

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Improving Knowledge Graph Embedding Using Simple Constraints
Boyang Ding | Quan Wang | Bin Wang | Li Guo
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Early works performed this task via simple models developed over KG triples. Recent attempts focused on either designing more complicated triple scoring models, or incorporating extra information beyond triples. This paper, by contrast, investigates the potential of using very simple constraints to improve KG embedding. We examine non-negativity constraints on entity representations and approximate entailment constraints on relation representations. The former help to learn compact and interpretable representations for entities. The latter further encode regularities of logical entailment between relations into their distributed representations. These constraints impose prior beliefs upon the structure of the embedding space, without negative impacts on efficiency or scalability. Evaluation on WordNet, Freebase, and DBpedia shows that our approach is simple yet surprisingly effective, significantly and consistently outperforming competitive baselines. The constraints imposed indeed improve model interpretability, leading to a substantially increased structuring of the embedding space. Code and data are available at https://github.com/iieir-km/ComplEx-NNE_AER.

2016

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Jointly Embedding Knowledge Graphs and Logical Rules
Shu Guo | Quan Wang | Lihong Wang | Bin Wang | Li Guo
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Multi-Granularity Chinese Word Embedding
Rongchao Yin | Quan Wang | Peng Li | Rui Li | Bin Wang
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Knowledge Base Completion via Coupled Path Ranking
Quan Wang | Jing Liu | Yuanfei Luo | Bin Wang | Chin-Yew Lin
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks
Xiaotian Jiang | Quan Wang | Peng Li | Bin Wang
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Distant supervision is an efficient approach that automatically generates labeled data for relation extraction (RE). Traditional distantly supervised RE systems rely heavily on handcrafted features, and hence suffer from error propagation. Recently, a neural network architecture has been proposed to automatically extract features for relation classification. However, this approach follows the traditional expressed-at-least-once assumption, and fails to make full use of information across different sentences. Moreover, it ignores the fact that there can be multiple relations holding between the same entity pair. In this paper, we propose a multi-instance multi-label convolutional neural network for distantly supervised RE. It first relaxes the expressed-at-least-once assumption, and employs cross-sentence max-pooling so as to enable information sharing across different sentences. Then it handles overlapping relations by multi-label learning with a neural network classifier. Experimental results show that our approach performs significantly and consistently better than state-of-the-art methods.

2015

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Context-Dependent Knowledge Graph Embedding
Yuanfei Luo | Quan Wang | Bin Wang | Li Guo
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Semantically Smooth Knowledge Graph Embedding
Shu Guo | Quan Wang | Bin Wang | Lihong Wang | Li Guo
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Trans-dimensional Random Fields for Language Modeling
Bin Wang | Zhijian Ou | Zhiqiang Tan
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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A Regularized Competition Model for Question Difficulty Estimation in Community Question Answering Services
Quan Wang | Jing Liu | Bin Wang | Li Guo
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2010

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Using Clustering to Improve Retrieval Evaluation without Relevance Judgments
Zhiwei Shi | Peng Li | Bin Wang
Coling 2010: Posters

2008

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A Study on Effectiveness of Syntactic Relationship in Dependence Retrieval Model
Fan Ding | Bin Wang
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

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Information Retrieval Oriented Word Segmentation based on Character Association Strength Ranking
Yixuan Liu | Bin Wang | Fan Ding | Sheng Xu
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2003

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Exploiting Parallel Texts for Word Sense Disambiguation: An Empirical Study
Hwee Tou Ng | Bin Wang | Yee Seng Chan
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics