Aiti Aw

Also published as: Ai Ti Aw, AiTi Aw


2023

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Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation
Xuan Long Do | Bowei Zou | Shafiq Joty | Tran Tai | Liangming Pan | Nancy Chen | Ai Ti Aw
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Conversational Question Generation (CQG) is a critical task for machines to assist humans in fulfilling their information needs through conversations. The task is generally cast into two different settings: answer-aware and answer-unaware. While the former facilitates the models by exposing the expected answer, the latter is more realistic and receiving growing attentions recently. What-to-ask and how-to-ask are the two main challenges in the answer-unaware setting. To address the first challenge, existing methods mainly select sequential sentences in context as the rationales. We argue that the conversation generated using such naive heuristics may not be natural enough as in reality, the interlocutors often talk about the relevant contents that are not necessarily sequential in context. Additionally, previous methods decide the type of question to be generated (boolean/span-based) implicitly. Modeling the question type explicitly is crucial as the answer, which hints the models to generate a boolean or span-based question, is unavailable. To this end, we present SG-CQG, a two-stage CQG framework. For the what-to-ask stage, a sentence is selected as the rationale from a semantic graph that we construct, and extract the answer span from it. For the how-to-ask stage, a classifier determines the target answer type of the question via two explicit control signals before generating and filtering. In addition, we propose Conv-Distinct, a novel evaluation metric for CQG, to evaluate the diversity of the generated conversation from a context. Compared with the existing answer-unaware CQG models, the proposed SG-CQG achieves state-of-the-art performance.

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DSPM-NLG: A Dual Supervised Pre-trained Model for Few-shot Natural Language Generation in Task-oriented Dialogue System
Yufan Wang | Bowei Zou | Rui Fan | Ai Ti Aw | Tingting He
Findings of the Association for Computational Linguistics: ACL 2023

In few-shot settings, fully conveying the semantic information of the dialogue act is a crucial challenge for Natural Language Generation (NLG) in the task-oriented dialogue system. An interesting fact is that NLG and Spoken Language Understanding (SLU) are a natural dual problem pair. Suppose the response generated by the NLG module can be restored to the corresponding dialogue act by the SLU module, which reflects that the generated response fully conveys the semantic information of the dialogue act. Based on this idea, a novel Dual Supervised Pre-trained Model for a few-shot Natural Language Generation (DSPM-NLG) is proposed to regularize the pre-training process. We adopt a joint model with a dual supervised framework to learn the dual correlation between NLG and SLU from the perspective of probability. In addition, a slot-masked strategy is designed to enable the model to focus better on the key slot-value pairs. DSPM-NLG is continuously trained on existing public large-scale annotated data, which thoroughly learns the duality between two tasks to enhance the semantically controlling and generalization abilities of the pre-trained model. Experiments demonstrate that our proposed model performs outstandingly on the few-shot benchmark dataset and outperforms the previous SOTA results.

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Making Pre-trained Language Models Better Learn Few-Shot Spoken Language Understanding in More Practical Scenarios
Yufan Wang | Jie Mei | Bowei Zou | Rui Fan | Tingting He | Ai Ti Aw
Findings of the Association for Computational Linguistics: ACL 2023

Most previous few-shot Spoken Language Understanding (SLU) models typically need to be trained on a set of data-rich source domains and adapt to the target domain with a few examples.In this paper, we explore a more practical scenario for few-shot SLU, in which we only assume access to a pre-trained language model and a few labeled examples without any other source domain data.We concentrate on understanding how far the few-shot SLU could be pushed in this setting.To this end, we develop a prompt-based intent detection model in few-shot settings, which leverages the BERT original pre-training next sentence prediction task and the prompt template to detect the user’s intent. For slot filling, we propose an approach of reconstructing slot labels, which reduces the training complexity by reducing the number of slot labels in few-shot settings.To evaluate the few-shot SLU for a more practical scenario, we present two benchmarks, FewShotATIS and FewShotSNIPS. And a dynamic sampling strategy is designed to construct the two datasets according to the learning difficulty of each intent and slot.Experiments on FewShotATIS and FewShotSNIPS demonstrate that our proposed model achieves state-of-the-art performance.

2022

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CoHS-CQG: Context and History Selection for Conversational Question Generation
Xuan Long Do | Bowei Zou | Liangming Pan | Nancy F. Chen | Shafiq Joty | Ai Ti Aw
Proceedings of the 29th International Conference on Computational Linguistics

Conversational question generation (CQG) serves as a vital task for machines to assist humans, such as interactive reading comprehension, through conversations. Compared to traditional single-turn question generation (SQG), CQG is more challenging in the sense that the generated question is required not only to be meaningful, but also to align with the provided conversation. Previous studies mainly focus on how to model the flow and alignment of the conversation, but do not thoroughly study which parts of the context and history are necessary for the model. We believe that shortening the context and history is crucial as it can help the model to optimise more on the conversational alignment property. To this end, we propose CoHS-CQG, a two-stage CQG framework, which adopts a novel CoHS module to shorten the context and history of the input. In particular, it selects the top-p sentences and history turns by calculating the relevance scores of them. Our model achieves state-of-the-art performances on CoQA in both the answer-aware and answer-unaware settings.

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CXR Data Annotation and Classification with Pre-trained Language Models
Nina Zhou | Ai Ti Aw | Zhuo Han Liu | Cher heng Tan | Yonghan Ting | Wen Xiang Chen | Jordan sim zheng Ting
Proceedings of the 29th International Conference on Computational Linguistics

Clinical data annotation has been one of the major obstacles for applying machine learning approaches in clinical NLP. Open-source tools such as NegBio and CheXpert are usually designed on data from specific institutions, which limit their applications to other institutions due to the differences in writing style, structure, language use as well as label definition. In this paper, we propose a new weak supervision annotation framework with two improvements compared to existing annotation frameworks: 1) we propose to select representative samples for efficient manual annotation; 2) we propose to auto-annotate the remaining samples, both leveraging on a self-trained sentence encoder. This framework also provides a function for identifying inconsistent annotation errors. The utility of our proposed weak supervision annotation framework is applicable to any given data annotation task, and it provides an efficient form of sample selection and data auto-annotation with better classification results for real applications.

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Singlish Message Paraphrasing: A Joint Task of Creole Translation and Text Normalization
Zhengyuan Liu | Shikang Ni | Ai Ti Aw | Nancy F. Chen
Proceedings of the 29th International Conference on Computational Linguistics

Within the natural language processing community, English is by far the most resource-rich language. There is emerging interest in conducting translation via computational approaches to conform its dialects or creole languages back to standard English. This computational approach paves the way to leverage generic English language backbones, which are beneficial for various downstream tasks. However, in practical online communication scenarios, the use of language varieties is often accompanied by noisy user-generated content, making this translation task more challenging. In this work, we introduce a joint paraphrasing task of creole translation and text normalization of Singlish messages, which can shed light on how to process other language varieties and dialects. We formulate the task in three different linguistic dimensions: lexical level normalization, syntactic level editing, and semantic level rewriting. We build an annotated dataset of Singlish-to-Standard English messages, and report performance on a perturbation-resilient sequence-to-sequence model. Experimental results show that the model produces reasonable generation results, and can improve the performance of downstream tasks like stance detection.

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Capturing Conversational Interaction for Question Answering via Global History Reasoning
Jin Qian | Bowei Zou | Mengxing Dong | Xiao Li | AiTi Aw | Yu Hong
Findings of the Association for Computational Linguistics: NAACL 2022

Conversational Question Answering (ConvQA) is required to answer the current question, conditioned on the observable paragraph-level context and conversation history. Previous works have intensively studied history-dependent reasoning. They perceive and absorb topic-related information of prior utterances in the interactive encoding stage. It yielded significant improvement compared to history-independent reasoning. This paper further strengthens the ConvQA encoder by establishing long-distance dependency among global utterances in multi-turn conversation. We use multi-layer transformers to resolve long-distance relationships, which potentially contribute to the reweighting of attentive information in historical utterances.Experiments on QuAC show that our method obtains a substantial improvement (1%), yielding the F1 score of 73.7%. All source codes are available at https://github.com/jaytsien/GHR.

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Automatic True/False Question Generation for Educational Purpose
Bowei Zou | Pengfei Li | Liangming Pan | Ai Ti Aw
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

In field of teaching, true/false questioning is an important educational method for assessing students’ general understanding of learning materials. Manually creating such questions requires extensive human effort and expert knowledge. Question Generation (QG) technique offers the possibility to automatically generate a large number of questions. However, there is limited work on automatic true/false question generation due to the lack of training data and difficulty finding question-worthy content. In this paper, we propose an unsupervised True/False Question Generation approach (TF-QG) that automatically generates true/false questions from a given passage for reading comprehension test. TF-QG consists of a template-based framework that aims to test the specific knowledge in the passage by leveraging various NLP techniques, and a generative framework to generate more flexible and complicated questions by using a novel masking-and-infilling strategy. Human evaluation shows that our approach can generate high-quality and valuable true/false questions. In addition, simulated testing on the generated questions challenges the state-of-the-art inference models from NLI, QA, and fact verification tasks.

2021

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Winnowing Knowledge for Multi-choice Question Answering
Yeqiu Li | Bowei Zou | Zhifeng Li | Ai Ti Aw | Yu Hong | Qiaoming Zhu
Findings of the Association for Computational Linguistics: EMNLP 2021

We tackle multi-choice question answering. Acquiring related commonsense knowledge to the question and options facilitates the recognition of the correct answer. However, the current reasoning models suffer from the noises in the retrieved knowledge. In this paper, we propose a novel encoding method which is able to conduct interception and soft filtering. This contributes to the harvesting and absorption of representative information with less interference from noises. We experiment on CommonsenseQA. Experimental results illustrate that our method yields substantial and consistent improvements compared to the strong Bert, RoBERTa and Albert-based baselines.

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Addressing the Vulnerability of NMT in Input Perturbations
Weiwen Xu | Ai Ti Aw | Yang Ding | Kui Wu | Shafiq Joty
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

Neural Machine Translation (NMT) has achieved significant breakthrough in performance but is known to suffer vulnerability to input perturbations. As real input noise is difficult to predict during training, robustness is a big issue for system deployment. In this paper, we improve the robustness of NMT models by reducing the effect of noisy words through a Context-Enhanced Reconstruction (CER) approach. CER trains the model to resist noise in two steps: (1) perturbation step that breaks the naturalness of input sequence with made-up words; (2) reconstruction step that defends the noise propagation by generating better and more robust contextual representation. Experimental results on Chinese-English (ZH-EN) and French-English (FR-EN) translation tasks demonstrate robustness improvement on both news and social media text. Further fine-tuning experiments on social media text show our approach can converge at a higher position and provide a better adaptation.

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Coherent and Concise Radiology Report Generation via Context Specific Image Representations and Orthogonal Sentence States
Litton J Kurisinkel | Ai Ti Aw | Nancy F Chen
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

Neural models for text generation are often designed in an end-to-end fashion, typically with zero control over intermediate computations, limiting their practical usability in downstream applications. In this work, we incorporate explicit means into neural models to ensure topical continuity, informativeness and content diversity of generated radiology reports. For the purpose we propose a method to compute image representations specific to each sentential context and eliminate redundant content by exploiting diverse sentence states. We conduct experiments to generate radiology reports from medical images of chest x-rays using MIMIC-CXR. Our model outperforms baselines by up to 18% and 29% respective in the evaluation for informativeness and content ordering respectively, relative on objective metrics and 16% on human evaluation.

2020

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NUT-RC: Noisy User-generated Text-oriented Reading Comprehension
Rongtao Huang | Bowei Zou | Yu Hong | Wei Zhang | AiTi Aw | Guodong Zhou
Proceedings of the 28th International Conference on Computational Linguistics

Reading comprehension (RC) on social media such as Twitter is a critical and challenging task due to its noisy, informal, but informative nature. Most existing RC models are developed on formal datasets such as news articles and Wikipedia documents, which severely limit their performances when directly applied to the noisy and informal texts in social media. Moreover, these models only focus on a certain type of RC, extractive or generative, but ignore the integration of them. To well address these challenges, we come up with a noisy user-generated text-oriented RC model. In particular, we first introduce a set of text normalizers to transform the noisy and informal texts to the formal ones. Then, we integrate the extractive and the generative RC model by a multi-task learning mechanism and an answer selection module. Experimental results on TweetQA demonstrate that our NUT-RC model significantly outperforms the state-of-the-art social media-oriented RC models.

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Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks
Zhengyuan Liu | Pavitra Krishnaswamy | Ai Ti Aw | Nancy Chen
Proceedings of the 28th International Conference on Computational Linguistics: Industry Track

While neural approaches have achieved significant improvement in machine comprehension tasks, models often work as a black-box, resulting in lower interpretability, which requires special attention in domains such as healthcare or education. Quantifying uncertainty helps pave the way towards more interpretable neural networks. In classification and regression tasks, Bayesian neural networks have been effective in estimating model uncertainty. However, inference time increases linearly due to the required sampling process in Bayesian neural networks. Thus speed becomes a bottleneck in tasks with high system complexity such as question-answering or dialogue generation. In this work, we propose a hybrid neural architecture to quantify model uncertainty using Bayesian weight approximation but boosts up the inference speed by 80% relative at test time, and apply it for a clinical dialogue comprehension task. The proposed approach is also used to enable active learning so that an updated model can be trained more optimally with new incoming data by selecting samples that are not well-represented in the current training scheme.

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GCDST: A Graph-based and Copy-augmented Multi-domain Dialogue State Tracking
Peng Wu | Bowei Zou | Ridong Jiang | AiTi Aw
Findings of the Association for Computational Linguistics: EMNLP 2020

As an essential component of task-oriented dialogue systems, Dialogue State Tracking (DST) takes charge of estimating user intentions and requests in dialogue contexts and extracting substantial goals (states) from user utterances to help the downstream modules to determine the next actions of dialogue systems. For practical usages, a major challenge to constructing a robust DST model is to process a conversation with multi-domain states. However, most existing approaches trained DST on a single domain independently, ignoring the information across domains. To tackle the multi-domain DST task, we first construct a dialogue state graph to transfer structured features among related domain-slot pairs across domains. Then, we encode the graph information of dialogue states by graph convolutional networks and utilize a hard copy mechanism to directly copy historical states from the previous conversation. Experimental results show that our model improves the performances of the multi-domain DST baseline (TRADE) with the absolute joint accuracy of 2.0% and 1.0% on the MultiWOZ 2.0 and 2.1 dialogue datasets, respectively.

2019

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Revisit Automatic Error Detection for Wrong and Missing Translation – A Supervised Approach
Wenqiang Lei | Weiwen Xu | Ai Ti Aw | Yuanxin Xiang | Tat Seng Chua
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

While achieving great fluency, current machine translation (MT) techniques are bottle-necked by adequacy issues. To have a closer study of these issues and accelerate model development, we propose automatic detecting adequacy errors in MT hypothesis for MT model evaluation. To do that, we annotate missing and wrong translations, the two most prevalent issues for current neural machine translation model, in 15000 Chinese-English translation pairs. We build a supervised alignment model for translation error detection (AlignDet) based on a simple Alignment Triangle strategy to set the benchmark for automatic error detection task. We also discuss the difficulties of this task and the benefits of this task for existing evaluation metrics.

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Negative Focus Detection via Contextual Attention Mechanism
Longxiang Shen | Bowei Zou | Yu Hong | Guodong Zhou | Qiaoming Zhu | AiTi Aw
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Negation is a universal but complicated linguistic phenomenon, which has received considerable attention from the NLP community over the last decade, since a negated statement often carries both an explicit negative focus and implicit positive meanings. For the sake of understanding a negated statement, it is critical to precisely detect the negative focus in context. However, how to capture contextual information for negative focus detection is still an open challenge. To well address this, we come up with an attention-based neural network to model contextual information. In particular, we introduce a framework which consists of a Bidirectional Long Short-Term Memory (BiLSTM) neural network and a Conditional Random Fields (CRF) layer to effectively encode the order information and the long-range context dependency in a sentence. Moreover, we design two types of attention mechanisms, word-level contextual attention and topic-level contextual attention, to take advantage of contextual information across sentences from both the word perspective and the topic perspective, respectively. Experimental results on the SEM’12 shared task corpus show that our approach achieves the best performance on negative focus detection, yielding an absolute improvement of 2.11% over the state-of-the-art. This demonstrates the great effectiveness of the two types of contextual attention mechanisms.

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Sentiment Aware Neural Machine Translation
Chenglei Si | Kui Wu | Ai Ti Aw | Min-Yen Kan
Proceedings of the 6th Workshop on Asian Translation

Sentiment ambiguous lexicons refer to words where their polarity depends strongly on con- text. As such, when the context is absent, their translations or their embedded sentence ends up (incorrectly) being dependent on the training data. While neural machine translation (NMT) has achieved great progress in recent years, most systems aim to produce one single correct translation for a given source sentence. We investigate the translation variation in two sentiment scenarios. We perform experiments to study the preservation of sentiment during translation with three different methods that we propose. We conducted tests with both sentiment and non-sentiment bearing contexts to examine the effectiveness of our methods. We show that NMT can generate both positive- and negative-valent translations of a source sentence, based on a given input sentiment label. Empirical evaluations show that our valence-sensitive embedding (VSE) method significantly outperforms a sequence-to-sequence (seq2seq) baseline, both in terms of BLEU score and ambiguous word translation accuracy in test, given non-sentiment bearing contexts.

2018

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Named-Entity Tagging and Domain adaptation for Better Customized Translation
Zhongwei Li | Xuancong Wang | Ai Ti Aw | Eng Siong Chng | Haizhou Li
Proceedings of the Seventh Named Entities Workshop

Customized translation need pay spe-cial attention to the target domain ter-minology especially the named-entities for the domain. Adding linguistic features to neural machine translation (NMT) has been shown to benefit translation in many studies. In this paper, we further demonstrate that adding named-entity (NE) feature with named-entity recognition (NER) into the source language produces better translation with NMT. Our experiments show that by just including the different NE classes and boundary tags, we can increase the BLEU score by around 1 to 2 points using the standard test sets from WMT2017. We also show that adding NE tags using NER and applying in-domain adaptation can be combined to further improve customized machine translation.

2016

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A Word Labeling Approach to Thai Sentence Boundary Detection and POS Tagging
Nina Zhou | AiTi Aw | Nattadaporn Lertcheva | Xuancong Wang
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Previous studies on Thai Sentence Boundary Detection (SBD) mostly assumed sentence ends at a space disambiguation problem, which classified space either as an indicator for Sentence Boundary (SB) or non-Sentence Boundary (nSB). In this paper, we propose a word labeling approach which treats space as a normal word, and detects SB between any two words. This removes the restriction for SB to be oc-curred only at space and makes our system more robust for modern Thai writing. It is because in modern Thai writing, space is not consistently used to indicate SB. As syntactic information contributes to better SBD, we further propose a joint Part-Of-Speech (POS) tagging and SBD framework based on Factorial Conditional Random Field (FCRF) model. We compare the performance of our proposed ap-proach with reported methods on ORCHID corpus. We also performed experiments of FCRF model on the TaLAPi corpus. The results show that the word labelling approach has better performance than pre-vious space-based classification approaches and FCRF joint model outperforms LCRF model in terms of SBD in all experiments.

2015

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Toward Tweets Normalization Using Maximum Entropy
Mohammad Arshi Saloot | Norisma Idris | Liyana Shuib | Ram Gopal Raj | AiTi Aw
Proceedings of the Workshop on Noisy User-generated Text

2014

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TaLAPi — A Thai Linguistically Annotated Corpus for Language Processing
AiTi Aw | Sharifah Mahani Aljunied | Nattadaporn Lertcheva | Sasiwimon Kalunsima
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper discusses a Thai corpus, TaLAPi, fully annotated with word segmentation (WS), part-of-speech (POS) and named entity (NE) information with the aim to provide a high-quality and sufficiently large corpus for real-life implementation of Thai language processing tools. The corpus contains 2,720 articles (1,043,471words) from the entertainment and lifestyle (NE&L) domain and 5,489 articles (3,181,487 words) in the news (NEWS) domain, with a total of 35 POS tags and 10 named entity categories. In particular, we present an approach to segment and tag foreign and loan words expressed in transliterated or original form in Thai text corpora. We see this as an area for study as adapted and un-adapted foreign language sequences have not been well addressed in the literature and this poses a challenge to the annotation process due to the increasing use and adoption of foreign words in the Thai language nowadays. To reduce the ambiguities in POS tagging and to provide rich information for facilitating Thai syntactic analysis, we adapted the POS tags used in ORCHID and propose a framework to tag Thai text and also addresses the tagging of loan and foreign words based on the proposed segmentation strategy. TaLAPi also includes a detailed guideline for tagging the 10 named entity categories

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A Rule-Augmented Statistical Phrase-based Translation System
Cong Duy Vu Hoang | AiTi Aw | Nhung T. H. Nguyen
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2012

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Personalized Normalization for a Multilingual Chat System
Ai Ti Aw | Lian Hau Lee
Proceedings of the ACL 2012 System Demonstrations

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An Unsupervised and Data-Driven Approach for Spell Checking in Vietnamese OCR-scanned Texts
Cong Duy Vu Hoang | Ai Ti Aw
Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data

2010

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Linguistically Annotated Reordering: Evaluation and Analysis
Deyi Xiong | Min Zhang | Aiti Aw | Haizhou Li
Computational Linguistics, Volume 36, Issue 3 - September 2010

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EM-based Hybrid Model for Bilingual Terminology Extraction from Comparable Corpora
Lianhau Lee | Aiti Aw | Min Zhang | Haizhou Li
Coling 2010: Posters

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I2R’s machine translation system for IWSLT 2010
Xiangyu Duan | Rafael Banchs | Jun Lang | Deyi Xiong | Aiti Aw | Min Zhang | Haizhou Li
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign

2009

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Efficient Beam Thresholding for Statistical Machine Translation
Deyi Xiong | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of Machine Translation Summit XII: Posters

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A Source Dependency Model for Statistical Machine Translation
Deyi Xiong | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of Machine Translation Summit XII: Posters

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Forest-based Tree Sequence to String Translation Model
Hui Zhang | Min Zhang | Haizhou Li | Aiti Aw | Chew Lim Tan
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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A Syntax-Driven Bracketing Model for Phrase-Based Translation
Deyi Xiong | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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A Comparative Study of Hypothesis Alignment and its Improvement for Machine Translation System Combination
Boxing Chen | Min Zhang | Haizhou Li | Aiti Aw
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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MARS: Multilingual Access and Retrieval System with Enhanced Query Translation and Document Retrieval
Lianhau Lee | Aiti Aw | Thuy Vu | Sharifah Aljunied Mahani | Min Zhang | Haizhou Li
Proceedings of the ACL-IJCNLP 2009 Software Demonstrations

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Feature-Based Method for Document Alignment in Comparable News Corpora
Thuy Vu | Ai Ti Aw | Min Zhang
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

2008

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Regenerating Hypotheses for Statistical Machine Translation
Boxing Chen | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Linguistically Annotated BTG for Statistical Machine Translation
Deyi Xiong | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Grammar Comparison Study for Translational Equivalence Modeling and Statistical Machine Translation
Min Zhang | Hongfei Jiang | Haizhou Li | Aiti Aw | Sheng Li
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Name Origin Recognition Using Maximum Entropy Model and Diverse Features
Min Zhang | Chengjie Sun | Haizhou Li | AiTi Aw | Chew Lim Tan | Xiaolong Wang
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

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Refinements in BTG-based Statistical Machine Translation
Deyi Xiong | Min Zhang | AiTi Aw | Haitao Mi | Qun Liu | Shouxun Lin
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

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Term Extraction Through Unithood and Termhood Unification
Thuy Vu | Ai Ti Aw | Min Zhang
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II

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Fast Computing Grammar-driven Convolution Tree Kernel for Semantic Role Labeling
Wanxiang Che | Min Zhang | Ai Ti Aw | Chew Lim Tan | Ting Liu | Sheng Li
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II

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A Tree Sequence Alignment-based Tree-to-Tree Translation Model
Min Zhang | Hongfei Jiang | Aiti Aw | Haizhou Li | Chew Lim Tan | Sheng Li
Proceedings of ACL-08: HLT

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A Linguistically Annotated Reordering Model for BTG-based Statistical Machine Translation
Deyi Xiong | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of ACL-08: HLT, Short Papers

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Exploiting N-best Hypotheses for SMT Self-Enhancement
Boxing Chen | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of ACL-08: HLT, Short Papers

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I2R multi-pass machine translation system for IWSLT 2008.
Boxing Chen | Deyi Xiong | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of the 5th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, we describe the system and approach used by the Institute for Infocomm Research (I2R) for the IWSLT 2008 spoken language translation evaluation campaign. In the system, we integrate various decoding algorithms into a multi-pass translation framework. The multi-pass approach enables us to utilize various decoding algorithm and to explore much more hypotheses. This paper reports our design philosophy, overall architecture, each individual system and various system combination methods that we have explored. The performance on development and test sets are reported in detail in the paper. The system has shown competitive performance with respect to the BLEU and METEOR measures in Chinese-English Challenge and BTEC tasks.

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The TALP&I2R SMT systems for IWSLT 2008.
Maxim Khalilov | Maria R. Costa-jussà | Carlos A. Henríquez Q. | José A. R. Fonollosa | Adolfo Hernández H. | José B. Mariño | Rafael E. Banchs | Chen Boxing | Min Zhang | Aiti Aw | Haizhou Li
Proceedings of the 5th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper gives a description of the statistical machine translation (SMT) systems developed at the TALP Research Center of the UPC (Universitat Polite`cnica de Catalunya) for our participation in the IWSLT’08 evaluation campaign. We present Ngram-based (TALPtuples) and phrase-based (TALPphrases) SMT systems. The paper explains the 2008 systems’ architecture and outlines translation schemes we have used, mainly focusing on the new techniques that are challenged to improve speech-to-speech translation quality. The novelties we have introduced are: improved reordering method, linear combination of translation and reordering models and new technique dealing with punctuation marks insertion for a phrase-based SMT system. This year we focus on the Arabic-English, Chinese-Spanish and pivot Chinese-(English)-Spanish translation tasks.

2007

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A Grammar-driven Convolution Tree Kernel for Semantic Role Classification
Min Zhang | Wanxiang Che | Aiti Aw | Chew Lim Tan | Guodong Zhou | Ting Liu | Sheng Li
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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A tree-to-tree alignment-based model for statistical machine translation
Min Zhang | Hongfei Jiang | Ai Ti Aw | Jun Sun | Sheng Li | Chew Lim Tan
Proceedings of Machine Translation Summit XI: Papers

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I2R Chinese-English translation system for IWSLT 2007
Boxing Chen | Jun Sun | Hongfei Jiang | Min Zhang | Ai Ti Aw
Proceedings of the Fourth International Workshop on Spoken Language Translation

In this paper, we describe the system and approach used by Institute for Infocomm Research (I2R) for the IWSLT 2007 spoken language evaluation campaign. A multi-pass approach is exploited to generate and select best translation. First, we use two decoders namely the open source Moses and an in-home syntax-based decoder to generate N-best lists. Next we spawn new translation entries through a word-based n-gram language model estimated on the former N-best entries. Finally, we join the N-best lists from the previous two passes, and select the best translation by rescoring them with additional feature functions. In particular, this paper reports our effort on new translation entry generation and system combination. The performance on development and test sets are reported. The system was ranked first with respect to the BLEU measure in Chinese-to-English open data track.

2006

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A Phrase-Based Statistical Model for SMS Text Normalization
AiTi Aw | Min Zhang | Juan Xiao | Jian Su
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions