Wei Chen


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MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding
Steven Wang | Antoine Scardigli | Leonard Tang | Wei Chen | Dmitry Levkin | Anya Chen | Spencer Ball | Thomas Woodside | Oliver Zhang | Dan Hendrycks
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association’s 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations. Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for significant improvement. As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.

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Dual Class Knowledge Propagation Network for Multi-label Few-shot Intent Detection
Feng Zhang | Wei Chen | Fei Ding | Tengjiao Wang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Multi-label intent detection aims to assign multiple labels to utterances and attracts increasing attention as a practical task in task-oriented dialogue systems. As dialogue domains change rapidly and new intents emerge fast, the lack of annotated data motivates multi-label few-shot intent detection. However, previous studies are confused by the identical representation of the utterance with multiple labels and overlook the intrinsic intra-class and inter-class interactions. To address these two limitations, we propose a novel dual class knowledge propagation network in this paper. In order to learn well-separated representations for utterances with multiple intents, we first introduce a label-semantic augmentation module incorporating class name information. For better consideration of the inherent intra-class and inter-class relations, an instance-level and a class-level graph neural network are constructed, which not only propagate label information but also propagate feature structure. And we use a simple yet effective method to predict the intent count of each utterance. Extensive experimental results on two multi-label intent datasets have demonstrated that our proposed method outperforms strong baselines by a large margin.

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KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition
Wei Chen | Shiqi Wei | Zhongyu Wei | Xuanjing Huang
Findings of the Association for Computational Linguistics: ACL 2023

Symptom diagnosis in medical conversations aims to correctly extract both symptom entities and their status from the doctor-patient dialogue. In this paper, we propose a novel framework called KNSE for symptom status recognition (SSR), where the SSR is formulated as a natural language inference (NLI) task. For each mentioned symptom in a dialogue window, we first generate knowledge about the symptom and hypothesis about status of the symptom, to form a (premise, knowledge, hypothesis) triplet. The BERT model is then used to encode the triplet, which is further processed by modules including utterance aggregation, self-attention, cross-attention, and GRU to predict the symptom status. Benefiting from the NLI formalization, the proposed framework can encode more informative prior knowledge to better localize and track symptom status, which can effectively improve the performance of symptom status recognition. Preliminary experiments on Chinese medical dialogue datasets show that KNSE outperforms previous competitive baselines and has advantages in cross-disease and cross-symptom scenarios.

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Towards Enhancing Relational Rules for Knowledge Graph Link Prediction
Shuhan Wu | Huaiyu Wan | Wei Chen | Yuting Wu | Junfeng Shen | Youfang Lin
Findings of the Association for Computational Linguistics: EMNLP 2023

Graph neural networks (GNNs) have shown promising performance for knowledge graph reasoning. A recent variant of GNN called progressive relational graph neural network (PRGNN), utilizes relational rules to infer missing knowledge in relational digraphs and achieves notable results. However, during reasoning with PRGNN, two important properties are often overlooked: (1) the sequentiality of relation composition, where the order of combining different relations affects the semantics of the relational rules, and (2) the lagged entity information propagation, where the transmission speed of required information lags behind the appearance speed of new entities. Ignoring these properties leads to incorrect relational rule learning and decreased reasoning accuracy. To address these issues, we propose a novel knowledge graph reasoning approach, the Relational rUle eNhanced Graph Neural Network (RUN-GNN). Specifically, RUN-GNN employs a query related fusion gate unit to model the sequentiality of relation composition and utilizes a buffering update mechanism to alleviate the negative effect of lagged entity information propagation, resulting in higher-quality relational rule learning. Experimental results on multiple datasets demonstrate the superiority of RUN-GNN is superior on both transductive and inductive link prediction tasks.


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FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation
Wenhao Zhu | Shujian Huang | Tong Pu | Pingxuan Huang | Xu Zhang | Jian Yu | Wei Chen | Yanfeng Wang | Jiajun Chen
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios. One representative of such challenging scenarios is to deploy a translation system for a conference with a specific topic, e.g., global warming or coronavirus, where there are usually extremely less resources due to the limited schedule. To motivate wider investigation in such a scenario, we present a real-world fine-grained domain adaptation task in machine translation (FGraDA). The FGraDA dataset consists of Chinese-English translation task for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone. Each sub-domain is equipped with a development set and test set for evaluation purposes. To be closer to reality, FGraDA does not employ any in-domain bilingual training data but provides bilingual dictionaries and wiki knowledge base, which can be easier obtained within a short time. We benchmark the fine-grained domain adaptation task and present in-depth analyses showing that there are still challenging problems to further improve the performance with heterogeneous resources.

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A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis
Wei Chen | Jinglong Du | Zhao Zhang | Fuzhen Zhuang | Zhongshi He
Proceedings of the 29th International Conference on Computational Linguistics

Recently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect boundaries and then classify the span-level sentiments (sentiment classification). However, most existing approaches either sequentially extract task-specific features, leading to insufficient feature interactions, or they encode aspect features and sentiment features in a parallel manner, implying that feature representation in each task is largely independent of each other except for input sharing. Both of them ignore the internal correlations between the aspect extraction and sentiment classification. To solve this problem, we novelly propose a hierarchical interactive network (HI-ASA) to model two-way interactions between two tasks appropriately, where the hierarchical interactions involve two steps: shallow-level interaction and deep-level interaction. First, we utilize cross-stitch mechanism to combine the different task-specific features selectively as the input to ensure proper two-way interactions. Second, the mutual information technique is applied to mutually constrain learning between two tasks in the output layer, thus the aspect input and the sentiment input are capable of encoding features of the other task via backpropagation. Extensive experiments on three real-world datasets demonstrate HI-ASA’s superiority over baselines.

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A Structure-Aware Argument Encoder for Literature Discourse Analysis
Yinzi Li | Wei Chen | Zhongyu Wei | Yujun Huang | Chujun Wang | Siyuan Wang | Qi Zhang | Xuanjing Huang | Libo Wu
Proceedings of the 29th International Conference on Computational Linguistics

Existing research for argument representation learning mainly treats tokens in the sentence equally and ignores the implied structure information of argumentative context. In this paper, we propose to separate tokens into two groups, namely framing tokens and topic ones, to capture structural information of arguments. In addition, we consider high-level structure by incorporating paragraph-level position information. A novel structure-aware argument encoder is proposed for literature discourse analysis. Experimental results on both a self-constructed corpus and a public corpus show the effectiveness of our model. Resources are available at https://github.com/lemuria-wchen/SAE.

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DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation
Wei Chen | Yeyun Gong | Song Wang | Bolun Yao | Weizhen Qi | Zhongyu Wei | Xiaowu Hu | Bartuer Zhou | Yi Mao | Weizhu Chen | Biao Cheng | Nan Duan
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Dialog response generation in open domain is an important research topic where the main challenge is to generate relevant and diverse responses. In this paper, we propose a new dialog pre-training framework called DialogVED, which introduces continuous latent variables into the enhanced encoder-decoder pre-training framework to increase the relevance and diversity of responses. With the help of a large dialog corpus (Reddit), we pre-train the model using the following 4 tasks, used in training language models (LMs) and Variational Autoencoders (VAEs) literature: 1) masked language model; 2) response generation; 3) bag-of-words prediction; and 4) KL divergence reduction. We also add additional parameters to model the turn structure in dialogs to improve the performance of the pre-trained model. We conduct experiments on PersonaChat, DailyDialog, and DSTC7-AVSD benchmarks for response generation. Experimental results show that our model achieves the new state-of-the-art results on all these datasets.

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Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations
Wei Chen | Yeyun Gong | Can Xu | Huang Hu | Bolun Yao | Zhongyu Wei | Zhihao Fan | Xiaowu Hu | Bartuer Zhou | Biao Cheng | Daxin Jiang | Nan Duan
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We study the problem of coarse-grained response selection in retrieval-based dialogue systems. The problem is equally important with fine-grained response selection, but is less explored in existing literature. In this paper, we propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations. In our CFC model, dense representations of query, candidate contexts and responses is learned based on the multi-tower architecture using contextual matching, and richer knowledge learned from the one-tower architecture (fine-grained) is distilled into the multi-tower architecture (coarse-grained) to enhance the performance of the retriever. To evaluate the performance of the proposed model, we construct two new datasets based on the Reddit comments dump and Twitter corpus. Extensive experimental results on the two datasets show that the proposed method achieves huge improvement over all evaluation metrics compared with traditional baseline methods.

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Learning Cooperative Interactions for Multi-Overlap Aspect Sentiment Triplet Extraction
Shiman Zhao | Wei Chen | Tengjiao Wang
Findings of the Association for Computational Linguistics: EMNLP 2022

Aspect sentiment triplet extraction (ASTE) is an essential task, which aims to extract triplets(aspect, opinion, sentiment). However, overlapped triplets, especially multi-overlap triplets,make ASTE a challenge. Most existing methods suffer from multi-overlap triplets becausethey focus on the single interactions between an aspect and an opinion. To solve the aboveissues, we propose a novel multi-overlap triplet extraction method, which decodes the complexrelations between multiple aspects and opinions by learning their cooperative interactions. Overall, the method is based on an encoder-decoder architecture. During decoding, we design ajoint decoding mechanism, which employs a multi-channel strategy to generate aspects andopinions through the cooperative interactions between them jointly. Furthermore, we constructa correlation-enhanced network to reinforce the interactions between related aspectsand opinions for sentiment prediction. Besides, a relation-wise calibration scheme is adoptedto further improve performance. Experiments show that our method outperforms baselines,especially multi-overlap triplets.


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Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network
Haoran Wu | Wei Chen | Shuang Xu | Bo Xu
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Providing a reliable explanation for clinical diagnosis based on the Electronic Medical Record (EMR) is fundamental to the application of Artificial Intelligence in the medical field. Current methods mostly treat the EMR as a text sequence and provide explanations based on a precise medical knowledge base, which is disease-specific and difficult to obtain for experts in reality. Therefore, we propose a counterfactual multi-granularity graph supporting facts extraction (CMGE) method to extract supporting facts from irregular EMR itself without external knowledge bases in this paper. Specifically, we first structure the sequence of EMR into a hierarchical graph network and then obtain the causal relationship between multi-granularity features and diagnosis results through counterfactual intervention on the graph. Features having the strongest causal connection with the results provide interpretive support for the diagnosis. Experimental results on real Chinese EMR of the lymphedema demonstrate that our method can diagnose four types of EMR correctly, and can provide accurate supporting facts for the results. More importantly, the results on different diseases demonstrate the robustness of our approach, which represents the potential application in the medical field.

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CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation
Chujie Zheng | Yong Liu | Wei Chen | Yongcai Leng | Minlie Huang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning
Hao Zhou | Minlie Huang | Yong Liu | Wei Chen | Xiaoyan Zhu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Generating informative and appropriate responses is challenging but important for building human-like dialogue systems. Although various knowledge-grounded conversation models have been proposed, these models have limitations in utilizing knowledge that infrequently occurs in the training data, not to mention integrating unseen knowledge into conversation generation. In this paper, we propose an Entity-Agnostic Representation Learning (EARL) method to introduce knowledge graphs to informative conversation generation. Unlike traditional approaches that parameterize the specific representation for each entity, EARL utilizes the context of conversations and the relational structure of knowledge graphs to learn the category representation for entities, which is generalized to incorporating unseen entities in knowledge graphs into conversation generation. Automatic and manual evaluations demonstrate that our model can generate more informative, coherent, and natural responses than baseline models.


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Robust Neural Machine Translation with ASR Errors
Haiyang Xue | Yang Feng | Shuhao Gu | Wei Chen
Proceedings of the First Workshop on Automatic Simultaneous Translation

In many practical applications, neural machine translation systems have to deal with the input from automatic speech recognition (ASR) systems which may contain a certain number of errors. This leads to two problems which degrade translation performance. One is the discrepancy between the training and testing data and the other is the translation error caused by the input errors may ruin the whole translation. In this paper, we propose a method to handle the two problems so as to generate robust translation to ASR errors. First, we simulate ASR errors in the training data so that the data distribution in the training and test is consistent. Second, we focus on ASR errors on homophone words and words with similar pronunciation and make use of their pronunciation information to help the translation model to recover from the input errors. Experiments on two Chinese-English data sets show that our method is more robust to input errors and can outperform the strong Transformer baseline significantly.


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Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
Zhen Yang | Wei Chen | Feng Wang | Bo Xu
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

This paper proposes an approach for applying GANs to NMT. We build a conditional sequence generative adversarial net which comprises of two adversarial sub models, a generator and a discriminator. The generator aims to generate sentences which are hard to be discriminated from human-translated sentences ( i.e., the golden target sentences); And the discriminator makes efforts to discriminate the machine-generated sentences from human-translated ones. The two sub models play a mini-max game and achieve the win-win situation when they reach a Nash Equilibrium. Additionally, the static sentence-level BLEU is utilized as the reinforced objective for the generator, which biases the generation towards high BLEU points. During training, both the dynamic discriminator and the static BLEU objective are employed to evaluate the generated sentences and feedback the evaluations to guide the learning of the generator. Experimental results show that the proposed model consistently outperforms the traditional RNNSearch and the newly emerged state-of-the-art Transformer on English-German and Chinese-English translation tasks.

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Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction
Jing Chen | Dechuan Yang | Xilian Li | Wei Chen | Tengjiao Wang
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes our participation in SemEval 2018 Task 2: Multilingual Emoji Prediction, in which participants are asked to predict a tweet’s most associated emoji from 20 emojis. Instead of regarding it as a 20-class classification problem we regard it as a text similarity problem. We propose a vector similarity based approach for this task. First the distributed representation (tweet vector) for each tweet is generated, then the similarity between this tweet vector and each emoji’s embedding is evaluated. The most similar emoji is chosen as the predicted label. Experimental results show that our approach performs comparably with the classification approach and shows its advantage in classifying emojis with similar semantic meaning.

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The Sogou-TIIC Speech Translation System for IWSLT 2018
Yuguang Wang | Liangliang Shi | Linyu Wei | Weifeng Zhu | Jinkun Chen | Zhichao Wang | Shixue Wen | Wei Chen | Yanfeng Wang | Jia Jia
Proceedings of the 15th International Conference on Spoken Language Translation

This paper describes our speech translation system for the IWSLT 2018 Speech Translation of lectures and TED talks from English to German task. The pipeline approach is employed in our work, which mainly includes the Automatic Speech Recognition (ASR) system, a post-processing module, and the Neural Machine Translation (NMT) system. Our ASR system is an ensemble system of Deep-CNN, BLSTM, TDNN, N-gram Language model with lattice rescoring. We report average results on tst2013, tst2014, tst2015. Our best combination system has an average WER of 6.73. The machine translation system is based on Google’s Transformer architecture. We achieved an improvement of 3.6 BLEU over baseline system by applying several techniques, such as cleaning parallel corpus, fine tuning of single model, ensemble models and re-scoring with additional features. Our final average result on speech translation is 31.02 BLEU.

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Unsupervised Neural Machine Translation with Weight Sharing
Zhen Yang | Wei Chen | Feng Wang | Bo Xu
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Unsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data. The models proposed for unsupervised NMT often use only one shared encoder to map the pairs of sentences from different languages to a shared-latent space, which is weak in keeping the unique and internal characteristics of each language, such as the style, terminology, and sentence structure. To address this issue, we introduce an extension by utilizing two independent encoders but sharing some partial weights which are responsible for extracting high-level representations of the input sentences. Besides, two different generative adversarial networks (GANs), namely the local GAN and global GAN, are proposed to enhance the cross-language translation. With this new approach, we achieve significant improvements on English-German, English-French and Chinese-to-English translation tasks.

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Semi-Supervised Disfluency Detection
Feng Wang | Wei Chen | Zhen Yang | Qianqian Dong | Shuang Xu | Bo Xu
Proceedings of the 27th International Conference on Computational Linguistics

While the disfluency detection has achieved notable success in the past years, it still severely suffers from the data scarcity. To tackle this problem, we propose a novel semi-supervised approach which can utilize large amounts of unlabelled data. In this work, a light-weight neural net is proposed to extract the hidden features based solely on self-attention without any Recurrent Neural Network (RNN) or Convolutional Neural Network (CNN). In addition, we use the unlabelled corpus to enhance the performance. Besides, the Generative Adversarial Network (GAN) training is applied to enforce the similar distribution between the labelled and unlabelled data. The experimental results show that our approach achieves significant improvements over strong baselines.


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Towards Compact and Fast Neural Machine Translation Using a Combined Method
Xiaowei Zhang | Wei Chen | Feng Wang | Shuang Xu | Bo Xu
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Neural Machine Translation (NMT) lays intensive burden on computation and memory cost. It is a challenge to deploy NMT models on the devices with limited computation and memory budgets. This paper presents a four stage pipeline to compress model and speed up the decoding for NMT. Our method first introduces a compact architecture based on convolutional encoder and weight shared embeddings. Then weight pruning is applied to obtain a sparse model. Next, we propose a fast sequence interpolation approach which enables the greedy decoding to achieve performance on par with the beam search. Hence, the time-consuming beam search can be replaced by simple greedy decoding. Finally, vocabulary selection is used to reduce the computation of softmax layer. Our final model achieves 10 times speedup, 17 times parameters reduction, less than 35MB storage size and comparable performance compared to the baseline model.

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Sogou Neural Machine Translation Systems for WMT17
Yuguang Wang | Shanbo Cheng | Liyang Jiang | Jiajun Yang | Wei Chen | Muze Li | Lin Shi | Yanfeng Wang | Hongtao Yang
Proceedings of the Second Conference on Machine Translation


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pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection
Wan Wei | Xiao Zhang | Xuqin Liu | Wei Chen | Tengjiao Wang
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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A Character-Aware Encoder for Neural Machine Translation
Zhen Yang | Wei Chen | Feng Wang | Bo Xu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This article proposes a novel character-aware neural machine translation (NMT) model that views the input sequences as sequences of characters rather than words. On the use of row convolution (Amodei et al., 2015), the encoder of the proposed model composes word-level information from the input sequences of characters automatically. Since our model doesn’t rely on the boundaries between each word (as the whitespace boundaries in English), it is also applied to languages without explicit word segmentations (like Chinese). Experimental results on Chinese-English translation tasks show that the proposed character-aware NMT model can achieve comparable translation performance with the traditional word based NMT models. Despite the target side is still word based, the proposed model is able to generate much less unknown words.


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Semi-supervised Chinese Word Segmentation based on Bilingual Information
Wei Chen | Bo Xu
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing


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Context-based Natural Language Processing for GIS-based Vague Region Visualization
Wei Chen
Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science

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Exploiting Community Emotion for Microblog Event Detection
Gaoyan Ou | Wei Chen | Tengjiao Wang | Zhongyu Wei | Binyang Li | Dongqing Yang | Kam-Fai Wong
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


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The CASIA machine translation system for IWSLT 2013
Xingyuan Peng | Xiaoyin Fu | Wei Wei | Zhenbiao Chen | Wei Chen | Bo Xu
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, we describe the CASIA statistical machine translation (SMT) system for the IWSLT2013 Evaluation Campaign. We participated in the Chinese-English and English-Chinese translation tasks. For both of these tasks, we used a hierarchical phrase-based (HPB) decoder and made it as our baseline translation system. A number of techniques were proposed to deal with these translation tasks, including parallel sentence extraction, pre-processing, translation model (TM) optimization, language model (LM) interpolation, turning, and post-processing. With these techniques, the translation results were significantly improved compared with that of the baseline system.

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Source aware phrase-based decoding for robust conversational spoken language translation
Sankaranarayanan Ananthakrishnan | Wei Chen | Rohit Kumar | Dennis Mehay
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

Spoken language translation (SLT) systems typically follow a pipeline architecture, in which the best automatic speech recognition (ASR) hypothesis of an input utterance is fed into a statistical machine translation (SMT) system. Conversational speech often generates unrecoverable ASR errors owing to its rich vocabulary (e.g. out-of-vocabulary (OOV) named entities). In this paper, we study the possibility of alleviating the impact of unrecoverable ASR errors on translation performance by minimizing the contextual effects of incorrect source words in target hypotheses. Our approach is driven by locally-derived penalties applied to bilingual phrase pairs as well as target language model (LM) likelihoods in the vicinity of source errors. With oracle word error labels on an OOV word-rich English-to-Iraqi Arabic translation task, we show statistically significant relative improvements of 3.2% BLEU and 2.0% METEOR over an error-agnostic baseline SMT system. We then investigate the impact of imperfect source error labels on error-aware translation performance. Simulation experiments reveal that modest translation improvements are to be gained with this approach even when the source error labels are noisy.


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Active error detection and resolution for speech-to-speech translation
Rohit Prasad | Rohit Kumar | Sankaranarayanan Ananthakrishnan | Wei Chen | Sanjika Hewavitharana | Matthew Roy | Frederick Choi | Aaron Challenner | Enoch Kan | Arvid Neelakantan | Prem Natarajan
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

We describe a novel two-way speech-to-speech (S2S) translation system that actively detects a wide variety of common error types and resolves them through user-friendly dialog with the user(s). We present algorithms for detecting out-of-vocabulary (OOV) named entities and terms, sense ambiguities, homophones, idioms, ill-formed input, etc. and discuss novel, interactive strategies for recovering from such errors. We also describe our approach for prioritizing different error types and an extensible architecture for implementing these decisions. We demonstrate the efficacy of our system by presenting analysis on live interactions in the English-to-Iraqi Arabic direction that are designed to invoke different error types for spoken language translation. Our analysis shows that the system can successfully resolve 47% of the errors, resulting in a dramatic improvement in the transfer of problematic concepts.


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Understanding Mental States in Natural Language
Wei Chen
Proceedings of the Eight International Conference on Computational Semantics


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Dimensions of Subjectivity in Natural Language
Wei Chen
Proceedings of ACL-08: HLT, Short Papers