Wen Wang

Also published as: W. Wang


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Advancing Precise Outline-Conditioned Text Generation with Task Duality and Explicit Outline Control
Yunzhe Li | Qian Chen | Weixiang Yan | Wen Wang | Qinglin Zhang | Hari Sundaram
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

Existing works on outline-conditioned text generation typically aim to generate text using provided outlines as rough sketches, such as keywords and phrases. However, these approaches make it challenging to control the quality of text generation and assess consistency between outlines and generated texts due to lack of clarity and rationality of the rough outlines. In this paper, we introduce a novel text generation task called Precise Outline-conditioned Generation, which requires generating stories based on specific, sentence-level outlines. To facilitate research on this task, we construct two new datasets, WPOG and CDM. We provide strong baselines based on fine-tuning models such as BART and GPT-2, and evaluating zero-shot performance of models such as ChatGPT and Vicuna. Furthermore, we identify an issue of imbalanced utilization of the outline information in the precise outline-conditioned generation, which is ubiquitously observed across fine-tuned models and zero-shot inference models. To address this issue, we propose an explicit outline utilization control approach and a novel framework that leverages the task duality between summarization and generation. Experimental results show that the proposed approaches effectively alleviate the issue of imbalanced outline utilization and enhance the quality of precise outline-conditioned text generation for both fine-tuning and zero-shot settings.


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DopplerBAS: Binaural Audio Synthesis Addressing Doppler Effect
Jinglin Liu | Zhenhui Ye | Qian Chen | Siqi Zheng | Wen Wang | Zhang Qinglin | Zhou Zhao
Findings of the Association for Computational Linguistics: ACL 2023

Recently, binaural audio synthesis (BAS) has emerged as a promising research field for its applications in augmented and virtual realities. Binaural audio helps ususers orient themselves and establish immersion by providing the brain with interaural time differences reflecting spatial information. However, existing BAS methods are limited in terms of phase estimation, which is crucial for spatial hearing. In this paper, we propose the DopplerBAS method to explicitly address the Doppler effect of the moving sound source. Specifically, we calculate the radial relative velocity of the moving speaker in spherical coordinates, which further guides the synthesis of binaural audio. This simple method introduces no additional hyper-parameters and does not modify the loss functions, and is plug-and-play: it scales well to different types of backbones. DopperBAS distinctly improves the representative WarpNet and BinauralGrad backbones in the phase error metric and reaches a new state of the art (SOTA): 0.780 (versus the current SOTA 0.807). Experiments and ablation studies demonstrate the effectiveness of our method.

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CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation
Weixiang Yan | Yuchen Tian | Yunzhe Li | Qian Chen | Wen Wang
Findings of the Association for Computational Linguistics: EMNLP 2023

Recent code translation techniques exploit neural machine translation models to translate source code from one programming language to another to satisfy production compatibility or to improve efficiency of codebase maintenance. Most existing code translation datasets only focus on a single pair of popular programming languages. To advance research on code translation and meet diverse requirements of real-world applications, we construct **CodeTransOcean**, a large-scale comprehensive benchmark that supports the largest variety of programming languages for code translation. CodeTransOcean consists of three novel multilingual datasets, namely, **MultilingualTrans** supporting translations between multiple popular programming languages, **NicheTrans** for translating between niche programming languages and popular ones, and **LLMTrans** for evaluating executability of translated code by large language models (LLMs). CodeTransOcean also includes a novel cross-framework dataset, **DLTrans**, for translating deep learning code across different frameworks. We develop multilingual modeling approaches for code translation and demonstrate their great potential in improving the translation quality of both low-resource and high-resource language pairs and boosting the training efficiency. We also propose a novel evaluation metric **Debugging Success Rate@K** for program-level code translation. Last but not least, we evaluate LLM ChatGPT on our datasets and investigate its potential for fuzzy execution predictions. We build baselines for CodeTransOcean and analyze challenges of code translation for guiding future research. The CodeTransOcean datasets and code are publicly available at https://github.com/WeixiangYAN/CodeTransOcean.

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DePA: Improving Non-autoregressive Translation with Dependency-Aware Decoder
Jiaao Zhan | Qian Chen | Boxing Chen | Wen Wang | Yu Bai | Yang Gao
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

Non-autoregressive machine translation (NAT) models have lower translation quality than autoregressive translation (AT) models because NAT decoders do not depend on previous target tokens in the decoder input. We propose a novel and general Dependency-Aware Decoder (DePA) to enhance target dependency modeling in the decoder of fully NAT models from two perspectives: decoder self-attention and decoder input. First, we propose an autoregressive forward-backward pre-training phase before NAT training, which enables the NAT decoder to gradually learn bidirectional target dependencies for the final NAT training. Second, we transform the decoder input from the source language representation space to the target language representation space through a novel attentive transformation process, which enables the decoder to better capture target dependencies. DePA can be applied to any fully NAT models. Extensive experiments show that DePA consistently improves highly competitive and state-of-the-art fully NAT models on widely used WMT and IWSLT benchmarks by up to 1.88 BLEU gain, while maintaining the inference latency comparable to other fully NAT models.

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Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling
Hai Yu | Chong Deng | Qinglin Zhang | Jiaqing Liu | Qian Chen | Wen Wang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic segmentation is critical for obtaining structured documents and improving down- stream tasks such as information retrieval. Due to its ability of automatically exploring clues of topic shift from abundant labeled data, recent supervised neural models have greatly promoted the development of long document topic segmentation, but leaving the deeper relationship between coherence and topic segmentation underexplored. Therefore, this paper enhances the ability of supervised models to capture coherence from both logical structure and semantic similarity perspectives to further improve the topic segmentation performance, proposing Topic-aware Sentence Structure Prediction (TSSP) and Contrastive Semantic Similarity Learning (CSSL). Specifically, the TSSP task is proposed to force the model to comprehend structural information by learning the original relations between adjacent sentences in a disarrayed document, which is constructed by jointly disrupting the original document at topic and sentence levels. Moreover, we utilize inter- and intra-topic information to construct contrastive samples and design the CSSL objective to ensure that the sentences representations in the same topic have higher similarity, while those in different topics are less similar. Extensive experiments show that the Longformer with our approach significantly outperforms old state-of-the-art (SOTA) methods. Our approach improve F1 of old SOTA by 3.42 (73.74 77.16) and reduces Pk by 1.11 points (15.0 13.89) on WIKI-727K and achieves an average relative reduction of 4.3% on Pk on WikiSection. The average relative Pk drop of 8.38% on two out-of-domain datasets also demonstrates the robustness of our approach.

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Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings
Qian Chen | Wen Wang | Qinglin Zhang | Siqi Zheng | Chong Deng | Hai Yu | Jiaqing Liu | Yukun Ma | Chong Zhang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Prior studies diagnose the anisotropy problem in sentence representations from pre-trained language models, e.g., BERT, without fine-tuning. Our analysis reveals that the sentence embeddings from BERT suffer from a bias towards uninformative words, limiting the performance in semantic textual similarity (STS) tasks. To address this bias, we propose a simple and efficient unsupervised approach, Diagonal Attention Pooling (Ditto), which weights words with model-based importance estimations and computes the weighted average of word representations from pre-trained models as sentence embeddings. Ditto can be easily applied to any pre-trained language model as a postprocessing operation. Compared to prior sentence embedding approaches, Ditto does not add parameters nor requires any learning. Empirical evaluations demonstrate that our proposed Ditto can alleviate the anisotropy problem and improve various pre-trained models on the STS benchmarks.


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MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction
Linhan Zhang | Qian Chen | Wen Wang | Chong Deng | ShiLiang Zhang | Bing Li | Wei Wang | Xin Cao
Findings of the Association for Computational Linguistics: ACL 2022

Keyphrase extraction (KPE) automatically extracts phrases in a document that provide a concise summary of the core content, which benefits downstream information retrieval and NLP tasks. Previous state-of-the-art methods select candidate keyphrases based on the similarity between learned representations of the candidates and the document. They suffer performance degradation on long documents due to discrepancy between sequence lengths which causes mismatch between representations of keyphrase candidates and the document. In this work, we propose a novel unsupervised embedding-based KPE approach, Masked Document Embedding Rank (MDERank), to address this problem by leveraging a mask strategy and ranking candidates by the similarity between embeddings of the source document and the masked document. We further develop a KPE-oriented BERT (KPEBERT) model by proposing a novel self-supervised contrastive learning method, which is more compatible to MDERank than vanilla BERT. Comprehensive evaluations on six KPE benchmarks demonstrate that the proposed MDERank outperforms state-of-the-art unsupervised KPE approach by average 1.80 F1@15 improvement. MDERank further benefits from KPEBERT and overall achieves average 3.53 F1@15 improvement over SIFRank.


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Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition
Yongliang Shen | Xinyin Ma | Zeqi Tan | Shuai Zhang | Wen Wang | Weiming Lu
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)

Named entity recognition (NER) is a well-studied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The span-based methods treat entity recognition as a span classification task. Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition. To tackle these issues, we propose a two-stage entity identifier. First we generate span proposals by filtering and boundary regression on the seed spans to locate the entities, and then label the boundary-adjusted span proposals with the corresponding categories. Our method effectively utilizes the boundary information of entities and partially matched spans during training. Through boundary regression, entities of any length can be covered theoretically, which improves the ability to recognize long entities. In addition, many low-quality seed spans are filtered out in the first stage, which reduces the time complexity of inference. Experiments on nested NER datasets demonstrate that our proposed method outperforms previous state-of-the-art models.


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Morphological Modeling for Machine Translation of English-Iraqi Arabic Spoken Dialogs
Katrin Kirchhoff | Yik-Cheung Tam | Colleen Richey | Wen Wang
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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A Cross-language Study on Automatic Speech Disfluency Detection
Wen Wang | Andreas Stolcke | Jiahong Yuan | Mark Liberman
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Name-aware Machine Translation
Haibo Li | Jing Zheng | Heng Ji | Qi Li | Wen Wang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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N-Best Rescoring Based on Pitch-accent Patterns
Je Hun Jeon | Wen Wang | Yang Liu
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Detection of Agreement and Disagreement in Broadcast Conversations
Wen Wang | Sibel Yaman | Kristin Precoda | Colleen Richey | Geoffrey Raymond
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


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Anchored Speech Recognition for Question Answering
Sibel Yaman | Gokhan Tur | Dimitra Vergyri | Dilek Hakkani-Tur | Mary Harper | Wen Wang
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers


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Improving Alignments for Better Confusion Networks for Combining Machine Translation Systems
Necip Fazil Ayan | Jing Zheng | Wen Wang
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)


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Mandarin Part-of-Speech Tagging and Discriminative Reranking
Zhongqiang Huang | Mary Harper | Wen Wang
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)


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A Statistical Constraint Dependency Grammar (CDG) Parser
Wen Wang | Mary P. Harper
Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together


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The SuperARV Language Model: Investigating the Effectiveness of Tightly Integrating Multiple Knowledge Sources
Wen Wang | Mary P. Harper
Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002)


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The Effectiveness of Corpus-Induced Dependency Grammars for Post-processing Speech
M. P. Harper | C. M. White | W. Wang | M. T. Johnson | R. A. Helzerman
1st Meeting of the North American Chapter of the Association for Computational Linguistics

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A Question Answering System Developed as a Project in a Natural Language Processing Course
W. Wang | J. Auer | R. Parasuraman | I. Zubarev | D. Brandyberry | M. P. Harper
ANLP-NAACL 2000 Workshop: Reading Comprehension Tests as Evaluation for Computer-Based Language Understanding Systems