Jing Liu


2024

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The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Education
Paiheng Xu | Jing Liu | Nathan Jones | Julie Cohen | Wei Ai
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Assessing instruction quality is a fundamental component of any improvement efforts in the education system. However, traditional manual assessments are expensive, subjective, and heavily dependent on observers’ expertise and idiosyncratic factors, preventing teachers from getting timely and frequent feedback. Different from prior research that mostly focuses on low-inference instructional practices on a singular basis, this paper presents the first study that leverages Natural Language Processing (NLP) techniques to assess multiple high-inference instructional practices in two distinct educational settings: in-person K-12 classrooms and simulated performance tasks for pre-service teachers. This is also the first study that applies NLP to measure a teaching practice that is widely acknowledged to be particularly effective for students with special needs. We confront two challenges inherent in NLP-based instructional analysis, including noisy and long input data and highly skewed distributions of human ratings. Our results suggest that pretrained Language Models (PLMs) demonstrate performances comparable to the agreement level of human raters for variables that are more discrete and require lower inference, but their efficacy diminishes with more complex teaching practices. Interestingly, using only teachers’ utterances as input yields strong results for student-centered variables, alleviating common concerns over the difficulty of collecting and transcribing high-quality student speech data in in-person teaching settings. Our findings highlight both the potential and the limitations of current NLP techniques in the education domain, opening avenues for further exploration.

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Beyond Literal Descriptions: Understanding and Locating Open-World Objects Aligned with Human Intentions
Wenxuan Wang | Yisi Zhang | Xingjian He | Yichen Yan | Zijia Zhao | Xinlong Wang | Jing Liu
Findings of the Association for Computational Linguistics ACL 2024

Visual grounding (VG) aims at locating the foreground entities that match the given natural language expression. Previous datasets and methods for classic VG task mainly rely on the prior assumption that the given expression must literally refer to the target object, which greatly impedes the practical deployment of agents in real-world scenarios. Since users usually prefer to provide the intention-based expressions for the desired object instead of covering all the details, it is necessary for the agents to interpret the intention-driven instructions. Thus, in this work, we take a step further to the intention-driven visual-language (V-L) understanding. To promote classic VG towards human intention interpretation, we propose a new intention-driven visual grounding (IVG) task and build a largest-scale IVG dataset named IntentionVG with free-form intention expressions. Considering that practical agents need to move and find specific targets among various scenarios to realize the grounding task, our IVG task and IntentionVG dataset have taken the crucial properties of both multi-scenario perception and egocentric view into consideration. Besides, various types of models are set up as the baselines to realize our IVG task. Extensive experiments on our IntentionVG dataset and baselines demonstrate the necessity and efficacy of our method for the V-L field. To foster future research in this direction, our newly built dataset and baselines will be publicly available at https://github.com/Rubics-Xuan/IVG.

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ConEC: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition
Ruizhe Huang | Mahsa Yarmohammadi | Jan Trmal | Jing Liu | Desh Raj | Leibny Paola Garcia | Alexei V. Ivanov | Patrick Ehlen | Mingzhi Yu | Dan Povey | Sanjeev Khudanpur
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Knowing the particular context associated with a conversation can help improving the performance of an automatic speech recognition (ASR) system. For example, if we are provided with a list of in-context words or phrases — such as the speaker’s contacts or recent song playlists — during inference, we can bias the recognition process towards this list. There are many works addressing contextual ASR; however, there is few publicly available real benchmark for evaluation, making it difficult to compare different solutions. To this end, we provide a corpus (“ConEC”) and baselines to evaluate contextual ASR approaches, grounded on real-world applications. The ConEC corpus is based on public-domain earnings calls (ECs) and associated supplementary materials, such as presentation slides, earnings news release as well as a list of meeting participants’ names and affiliations. We demonstrate that such real contexts are noisier than artificially synthesized contexts that contain the ground truth, yet they still make great room for future improvement of contextual ASR technology

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Soft Knowledge Prompt: Help External Knowledge Become a Better Teacher to Instruct LLM in Knowledge-based VQA
Qunbo Wang | Ruyi Ji | Tianhao Peng | Wenjun Wu | Zechao Li | Jing Liu
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

LLM has achieved impressive performance on multi-modal tasks, which have received ever-increasing research attention. Recent research focuses on improving prediction performance and reliability (e.g., addressing the hallucination problem). They often prepend relevant external knowledge to the input text as an extra prompt. However, these methods would be affected by the noise in the knowledge and the context length limitation of LLM. In our work, we focus on making better use of external knowledge and propose a method to actively extract valuable information in the knowledge to produce the latent vector as a soft prompt, which is then fused with the image embedding to form a knowledge-enhanced context to instruct LLM. The experimental results on knowledge-based VQA benchmarks show that the proposed method enjoys better utilization of external knowledge and helps the model achieve better performance.

2023

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A Thorough Examination on Zero-shot Dense Retrieval
Ruiyang Ren | Yingqi Qu | Jing Liu | Xin Zhao | Qifei Wu | Yuchen Ding | Hua Wu | Haifeng Wang | Ji-Rong Wen
Findings of the Association for Computational Linguistics: EMNLP 2023

Recent years have witnessed the significant advance in dense retrieval (DR) based on powerful pre-trained language models (PLM). DR models have achieved excellent performance in several benchmark datasets, while they are shown to be not as competitive as traditional sparse retrieval models (e.g., BM25) in a zero-shot retrieval setting. However, in the related literature, there still lacks a detailed and comprehensive study on zero-shot retrieval. In this paper, we present the first thorough examination of the zero-shot capability of DR models. We aim to identify the key factors and analyze how they affect zero-shot retrieval performance. In particular, we discuss the effect of several key factors related to source training set, analyze the potential bias from the target dataset, and review and compare existing zero-shot DR models. Our findings provide important evidence to better understand and develop zero-shot DR models.

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TOME: A Two-stage Approach for Model-based Retrieval
Ruiyang Ren | Wayne Xin Zhao | Jing Liu | Hua Wu | Ji-Rong Wen | Haifeng Wang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Recently, model-based retrieval has emerged as a new paradigm in text retrieval that discards the index in the traditional retrieval model and instead memorizes the candidate corpora using model parameters. This design employs a sequence-to-sequence paradigm to generate document identifiers, which enables the complete capture of the relevance between queries and documents and simplifies the classic index-retrieval-rerank pipeline. Despite its attractive qualities, there remain several major challenges in model-based retrieval, including the discrepancy between pre-training and fine-tuning, and the discrepancy between training and inference. To deal with the above challenges, we propose a novel two-stage model-based retrieval approach called TOME, which makes two major technical contributions, including the utilization of tokenized URLs as identifiers and the design of a two-stage generation architecture. We also propose a number of training strategies to deal with the training difficulty as the corpus size increases. Extensive experiments and analysis on MS MARCO and Natural Questions demonstrate the effectiveness of our proposed approach, and we investigate the scaling laws of TOME by examining various influencing factors.

2022

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基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)
Shuang Nie (聂双) | Zheng Ye (叶正) | Jun Qin (覃俊) | Jing Liu (刘晶)
Proceedings of the 21st Chinese National Conference on Computational Linguistics

“目前常见的机器阅读理解数据增强方法如回译,单独对文章或者问题进行数据增强,没有考虑文章、问题和选项三元组之间的联系。因此,本文探索了一种利用三元组联系进行文章句子筛选的数据增强方法,通过比较文章与问题以及选项的相似度,选取文章中与二者联系紧密的句子。同时为了使不同选项的三元组区别增大,我们选用了正则化Dropout的策略。实验结果表明,在RACE数据集上的准确率可提高3.8%。”

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DuReader-Retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine
Yifu Qiu | Hongyu Li | Yingqi Qu | Ying Chen | QiaoQiao She | Jing Liu | Hua Wu | Haifeng Wang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

In this paper, we present DuReader-retrieval, a large-scale Chinese dataset for passage retrieval. DuReader-retrieval contains more than 90K queries and over 8M unique passages from a commercial search engine. To alleviate the shortcomings of other datasets and ensure the quality of our benchmark, we (1) reduce the false negatives in development and test sets by manually annotating results pooled from multiple retrievers, and (2) remove the training queries that are semantically similar to the development and testing queries. Additionally, we provide two out-of-domain testing sets for cross-domain evaluation, as well as a set of human translated queries for for cross-lingual retrieval evaluation. The experiments demonstrate that DuReader-retrieval is challenging and a number of problems remain unsolved, such as the salient phrase mismatch and the syntactic mismatch between queries and paragraphs. These experiments also show that dense retrievers do not generalize well across domains, and cross-lingual retrieval is essentially challenging. DuReader-retrieval is publicly available at https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval.

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DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models
Hongyu Zhu | Yan Chen | Jing Yan | Jing Liu | Yu Hong | Ying Chen | Hua Wu | Haifeng Wang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

In this paper, we focus on the robustness evaluation of Chinese Question Matching (QM) models. Most of the previous work on analyzing robustness issues focus on just one or a few types of artificial adversarial examples. Instead, we argue that a comprehensive evaluation should be conducted on natural texts, which takes into account the fine-grained linguistic capabilities of QM models. For this purpose, we create a Chinese dataset namely DuQM which contains natural questions with linguistic perturbations to evaluate the robustness of QM models. DuQM contains 3 categories and 13 subcategories with 32 linguistic perturbations. The extensive experiments demonstrate that DuQM has a better ability to distinguish different models. Importantly, the detailed breakdown of evaluation by the linguistic phenomena in DuQM helps us easily diagnose the strength and weakness of different models. Additionally, our experiment results show that the effect of artificial adversarial examples does not work on natural texts. Our baseline codes and a leaderboard are now publicly available.

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DuReadervis: A Chinese Dataset for Open-domain Document Visual Question Answering
Le Qi | Shangwen Lv | Hongyu Li | Jing Liu | Yu Zhang | Qiaoqiao She | Hua Wu | Haifeng Wang | Ting Liu
Findings of the Association for Computational Linguistics: ACL 2022

Open-domain question answering has been used in a wide range of applications, such as web search and enterprise search, which usually takes clean texts extracted from various formats of documents (e.g., web pages, PDFs, or Word documents) as the information source. However, designing different text extraction approaches is time-consuming and not scalable. In order to reduce human cost and improve the scalability of QA systems, we propose and study an Open-domain Document Visual Question Answering (Open-domain DocVQA) task, which requires answering questions based on a collection of document images directly instead of only document texts, utilizing layouts and visual features additionally. Towards this end, we introduce the first Chinese Open-domain DocVQA dataset called DuReadervis, containing about 15K question-answering pairs and 158K document images from the Baidu search engine. There are three main challenges in DuReadervis: (1) long document understanding, (2) noisy texts, and (3) multi-span answer extraction. The extensive experiments demonstrate that the dataset is challenging. Additionally, we propose a simple approach that incorporates the layout and visual features, and the experimental results show the effectiveness of the proposed approach. The dataset and code will be publicly available at https://github.com/baidu/DuReader/tree/master/DuReader-vis.

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Computationally Identifying Funneling and Focusing Questions in Classroom Discourse
Sterling Alic | Dorottya Demszky | Zid Mancenido | Jing Liu | Heather Hill | Dan Jurafsky
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

Responsive teaching is a highly effective strategy that promotes student learning. In math classrooms, teachers might funnel students towards a normative answer or focus students to reflect on their own thinking depending their understanding of math concepts. When teachers focus, they treat students’ contributions as resources for collective sensemaking, and thereby significantly improve students’ achievement and confidence in mathematics. We propose the task of computationally detecting funneling and focusing questions in classroom discourse. We do so by creating and releasing an annotated dataset of 2,348 teacher utterances labeled for funneling and focusing questions, or neither. We introduce supervised and unsupervised approaches to differentiating these questions. Our best model, a supervised RoBERTa model fine-tuned on our dataset, has a strong linear correlation of .76 with human expert labels and with positive educational outcomes, including math instruction quality and student achievement, showing the model’s potential for use in automated teacher feedback tools. Our unsupervised measures show significant but weaker correlations with human labels and outcomes, and they highlight interesting linguistic patterns of funneling and focusing questions. The high performance of the supervised measure indicates its promise for supporting teachers in their instruction.

2021

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RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering
Yingqi Qu | Yuchen Ding | Jing Liu | Kai Liu | Ruiyang Ren | Wayne Xin Zhao | Daxiang Dong | Hua Wu | Haifeng Wang
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In open-domain question answering, dense passage retrieval has become a new paradigm to retrieve relevant passages for finding answers. Typically, the dual-encoder architecture is adopted to learn dense representations of questions and passages for semantic matching. However, it is difficult to effectively train a dual-encoder due to the challenges including the discrepancy between training and inference, the existence of unlabeled positives and limited training data. To address these challenges, we propose an optimized training approach, called RocketQA, to improving dense passage retrieval. We make three major technical contributions in RocketQA, namely cross-batch negatives, denoised hard negatives and data augmentation. The experiment results show that RocketQA significantly outperforms previous state-of-the-art models on both MSMARCO and Natural Questions. We also conduct extensive experiments to examine the effectiveness of the three strategies in RocketQA. Besides, we demonstrate that the performance of end-to-end QA can be improved based on our RocketQA retriever.

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RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
Ruiyang Ren | Yingqi Qu | Jing Liu | Wayne Xin Zhao | QiaoQiao She | Hua Wu | Haifeng Wang | Ji-Rong Wen
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In various natural language processing tasks, passage retrieval and passage re-ranking are two key procedures in finding and ranking relevant information. Since both the two procedures contribute to the final performance, it is important to jointly optimize them in order to achieve mutual improvement. In this paper, we propose a novel joint training approach for dense passage retrieval and passage reranking. A major contribution is that we introduce the dynamic listwise distillation, where we design a unified listwise training approach for both the retriever and the re-ranker. During the dynamic distillation, the retriever and the re-ranker can be adaptively improved according to each other’s relevance information. We also propose a hybrid data augmentation strategy to construct diverse training instances for listwise training approach. Extensive experiments show the effectiveness of our approach on both MSMARCO and Natural Questions datasets. Our code is available at https://github.com/PaddlePaddle/RocketQA.

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PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval
Ruiyang Ren | Shangwen Lv | Yingqi Qu | Jing Liu | Wayne Xin Zhao | QiaoQiao She | Hua Wu | Haifeng Wang | Ji-Rong Wen
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions
Dorottya Demszky | Jing Liu | Zid Mancenido | Julie Cohen | Heather Hill | Dan Jurafsky | Tatsunori Hashimoto
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In conversation, uptake happens when a speaker builds on the contribution of their interlocutor by, for example, acknowledging, repeating or reformulating what they have said. In education, teachers’ uptake of student contributions has been linked to higher student achievement. Yet measuring and improving teachers’ uptake at scale is challenging, as existing methods require expensive annotation by experts. We propose a framework for computationally measuring uptake, by (1) releasing a dataset of student-teacher exchanges extracted from US math classroom transcripts annotated for uptake by experts; (2) formalizing uptake as pointwise Jensen-Shannon Divergence (pJSD), estimated via next utterance classification; (3) conducting a linguistically-motivated comparison of different unsupervised measures and (4) correlating these measures with educational outcomes. We find that although repetition captures a significant part of uptake, pJSD outperforms repetition-based baselines, as it is capable of identifying a wider range of uptake phenomena like question answering and reformulation. We apply our uptake measure to three different educational datasets with outcome indicators. Unlike baseline measures, pJSD correlates significantly with instruction quality in all three, providing evidence for its generalizability and for its potential to serve as an automated professional development tool for teachers.

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DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications
Hongxuan Tang | Hongyu Li | Jing Liu | Yu Hong | Hua Wu | Haifeng Wang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Machine reading comprehension (MRC) is a crucial task in natural language processing and has achieved remarkable advancements. However, most of the neural MRC models are still far from robust and fail to generalize well in real-world applications. In order to comprehensively verify the robustness and generalization of MRC models, we introduce a real-world Chinese dataset – DuReader_robust . It is designed to evaluate the MRC models from three aspects: over-sensitivity, over-stability and generalization. Comparing to previous work, the instances in DuReader_robust are natural texts, rather than the altered unnatural texts. It presents the challenges when applying MRC models to real-world applications. The experimental results show that MRC models do not perform well on the challenge test set. Moreover, we analyze the behavior of existing models on the challenge test set, which may provide suggestions for future model development. The dataset and codes are publicly available at https://github.com/baidu/DuReader.

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基于BERT的意图分类与槽填充联合方法(Joint Method of Intention Classification and Slot Filling Based on BERT)
Jun Qin (覃俊) | Tianyu Ma (马天宇) | Jing Liu (刘晶) | Jun Tie (帖军) | Qi Hou (后琦)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

口语理解是自然语言处理的一个重要内容,意图分类和槽填充是口语理解的两个基本子任务。最近的研究表明,共同学习这两项任务可以起到相互促进的作用。本文提出了一个基于BERT的意图分类联合模型,通过一个关联网络使得两个任务建立直接联系,共享信息,以此来提升任务效果。模型引入BERT来增强词向量的语义表示,有效解决了目前联合模型由于训练数据规模较小导致的泛化能力较差的问题。实验结果表明,该模型能有效提升意图分类和槽填充的性能。

2019

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Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension
An Yang | Quan Wang | Jing Liu | Kai Liu | Yajuan Lyu | Hua Wu | Qiaoqiao She | Sujian Li
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Machine reading comprehension (MRC) is a crucial and challenging task in NLP. Recently, pre-trained language models (LMs), especially BERT, have achieved remarkable success, presenting new state-of-the-art results in MRC. In this work, we investigate the potential of leveraging external knowledge bases (KBs) to further improve BERT for MRC. We introduce KT-NET, which employs an attention mechanism to adaptively select desired knowledge from KBs, and then fuses selected knowledge with BERT to enable context- and knowledge-aware predictions. We believe this would combine the merits of both deep LMs and curated KBs towards better MRC. Experimental results indicate that KT-NET offers significant and consistent improvements over BERT, outperforming competitive baselines on ReCoRD and SQuAD1.1 benchmarks. Notably, it ranks the 1st place on the ReCoRD leaderboard, and is also the best single model on the SQuAD1.1 leaderboard at the time of submission (March 4th, 2019).

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D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension
Hongyu Li | Xiyuan Zhang | Yibing Liu | Yiming Zhang | Quan Wang | Xiangyang Zhou | Jing Liu | Hua Wu | Haifeng Wang
Proceedings of the 2nd Workshop on Machine Reading for Question Answering

In this paper, we introduce a simple system Baidu submitted for MRQA (Machine Reading for Question Answering) 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models. Our system is built on a framework of pretraining and fine-tuning, namely D-NET. The techniques of pre-trained language models and multi-task learning are explored to improve the generalization of MRC models and we conduct experiments to examine the effectiveness of these strategies. Our system is ranked at top 1 of all the participants in terms of averaged F1 score. Our codes and models will be released at PaddleNLP.

2018

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DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
Wei He | Kai Liu | Jing Liu | Yajuan Lyu | Shiqi Zhao | Xinyan Xiao | Yuan Liu | Yizhong Wang | Hua Wu | Qiaoqiao She | Xuan Liu | Tian Wu | Haifeng Wang
Proceedings of the Workshop on Machine Reading for Question Answering

This paper introduces DuReader, a new large-scale, open-domain Chinese machine reading comprehension (MRC) dataset, designed to address real-world MRC. DuReader has three advantages over previous MRC datasets: (1) data sources: questions and documents are based on Baidu Search and Baidu Zhidao; answers are manually generated. (2) question types: it provides rich annotations for more question types, especially yes-no and opinion questions, that leaves more opportunity for the research community. (3) scale: it contains 200K questions, 420K answers and 1M documents; it is the largest Chinese MRC dataset so far. Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements. To help the community make these improvements, both DuReader and baseline systems have been posted online. We also organize a shared competition to encourage the exploration of more models. Since the release of the task, there are significant improvements over the baselines.

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Adaptations of ROUGE and BLEU to Better Evaluate Machine Reading Comprehension Task
An Yang | Kai Liu | Jing Liu | Yajuan Lyu | Sujian Li
Proceedings of the Workshop on Machine Reading for Question Answering

Current evaluation metrics to question answering based machine reading comprehension (MRC) systems generally focus on the lexical overlap between candidate and reference answers, such as ROUGE and BLEU. However, bias may appear when these metrics are used for specific question types, especially questions inquiring yes-no opinions and entity lists. In this paper, we make adaptations on the metrics to better correlate n-gram overlap with the human judgment for answers to these two question types. Statistical analysis proves the effectiveness of our approach. Our adaptations may provide positive guidance for the development of real-scene MRC systems.

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Neural Math Word Problem Solver with Reinforcement Learning
Danqing Huang | Jing Liu | Chin-Yew Lin | Jian Yin
Proceedings of the 27th International Conference on Computational Linguistics

Sequence-to-sequence model has been applied to solve math word problems. The model takes math problem descriptions as input and generates equations as output. The advantage of sequence-to-sequence model requires no feature engineering and can generate equations that do not exist in training data. However, our experimental analysis reveals that this model suffers from two shortcomings: (1) generate spurious numbers; (2) generate numbers at wrong positions. In this paper, we propose incorporating copy and alignment mechanism to the sequence-to-sequence model (namely CASS) to address these shortcomings. To train our model, we apply reinforcement learning to directly optimize the solution accuracy. It overcomes the “train-test discrepancy” issue of maximum likelihood estimation, which uses the surrogate objective of maximizing equation likelihood during training while the evaluation metric is solution accuracy (non-differentiable) at test time. Furthermore, to explore the effectiveness of our neural model, we use our model output as a feature and incorporate it into the feature-based model. Experimental results show that (1) The copy and alignment mechanism is effective to address the two issues; (2) Reinforcement learning leads to better performance than maximum likelihood on this task; (3) Our neural model is complementary to the feature-based model and their combination significantly outperforms the state-of-the-art results.

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Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification
Yizhong Wang | Kai Liu | Jing Liu | Wei He | Yajuan Lyu | Hua Wu | Sujian Li | Haifeng Wang
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine. Compared with MRC on a single passage, multi-passage MRC is more challenging, since we are likely to get multiple confusing answer candidates from different passages. To address this problem, we propose an end-to-end neural model that enables those answer candidates from different passages to verify each other based on their content representations. Specifically, we jointly train three modules that can predict the final answer based on three factors: the answer boundary, the answer content and the cross-passage answer verification. The experimental results show that our method outperforms the baseline by a large margin and achieves the state-of-the-art performance on the English MS-MARCO dataset and the Chinese DuReader dataset, both of which are designed for MRC in real-world settings.

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Revisiting Distant Supervision for Relation Extraction
Tingsong Jiang | Jing Liu | Chin-Yew Lin | Zhifang Sui
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Aggregated Semantic Matching for Short Text Entity Linking
Feng Nie | Shuyan Zhou | Jing Liu | Jinpeng Wang | Chin-Yew Lin | Rong Pan
Proceedings of the 22nd Conference on Computational Natural Language Learning

The task of entity linking aims to identify concepts mentioned in a text fragments and link them to a reference knowledge base. Entity linking in long text has been well studied in previous work. However, short text entity linking is more challenging since the text are noisy and less coherent. To better utilize the local information provided in short texts, we propose a novel neural network framework, Aggregated Semantic Matching (ASM), in which two different aspects of semantic information between the local context and the candidate entity are captured via representation-based and interaction-based neural semantic matching models, and then two matching signals work jointly for disambiguation with a rank aggregation mechanism. Our evaluation shows that the proposed model outperforms the state-of-the-arts on public tweet datasets.

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Answer-focused and Position-aware Neural Question Generation
Xingwu Sun | Jing Liu | Yajuan Lyu | Wei He | Yanjun Ma | Shi Wang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

In this paper, we focus on the problem of question generation (QG). Recent neural network-based approaches employ the sequence-to-sequence model which takes an answer and its context as input and generates a relevant question as output. However, we observe two major issues with these approaches: (1) The generated interrogative words (or question words) do not match the answer type. (2) The model copies the context words that are far from and irrelevant to the answer, instead of the words that are close and relevant to the answer. To address these two issues, we propose an answer-focused and position-aware neural question generation model. (1) By answer-focused, we mean that we explicitly model question word generation by incorporating the answer embedding, which can help generate an interrogative word matching the answer type. (2) By position-aware, we mean that we model the relative distance between the context words and the answer. Hence the model can be aware of the position of the context words when copying them to generate a question. We conduct extensive experiments to examine the effectiveness of our model. The experimental results show that our model significantly improves the baseline and outperforms the state-of-the-art system.

2017

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A Statistical Framework for Product Description Generation
Jinpeng Wang | Yutai Hou | Jing Liu | Yunbo Cao | Chin-Yew Lin
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We present in this paper a statistical framework that generates accurate and fluent product description from product attributes. Specifically, after extracting templates and learning writing knowledge from attribute-description parallel data, we use the learned knowledge to decide what to say and how to say for product description generation. To evaluate accuracy and fluency for the generated descriptions, in addition to BLEU and Recall, we propose to measure what to say (in terms of attribute coverage) and to measure how to say (by attribute-specified generation) separately. Experimental results show that our framework is effective.

2016

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News Citation Recommendation with Implicit and Explicit Semantics
Hao Peng | Jing Liu | Chin-Yew Lin
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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RBPB: Regularization-Based Pattern Balancing Method for Event Extraction
Lei Sha | Jing Liu | Chin-Yew Lin | Sujian Li | Baobao Chang | Zhifang Sui
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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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)

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)

2013

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Question Difficulty Estimation in Community Question Answering Services
Jing Liu | Quan Wang | Chin-Yew Lin | Hsiao-Wuen Hon
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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A Hierarchical Entity-Based Approach to Structuralize User Generated Content in Social Media: A Case of Yahoo! Answers
Baichuan Li | Jing Liu | Chin-Yew Lin | Irwin King | Michael R. Lyu
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2011

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Nonlinear Evidence Fusion and Propagation for Hyponymy Relation Mining
Fan Zhang | Shuming Shi | Jing Liu | Shuqi Sun | Chin-Yew Lin
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies