Helen Meng

Also published as: Helen M. Meng


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Partner Personas Generation for Dialogue Response Generation
Hongyuan Lu | Wai Lam | Hong Cheng | Helen Meng
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Incorporating personas information allows diverse and engaging responses in dialogue response generation. Unfortunately, prior works have primarily focused on self personas and have overlooked the value of partner personas. Moreover, in practical applications, the availability of the gold partner personas is often not the case. This paper attempts to tackle these issues by offering a novel framework that leverages automatic partner personas generation to enhance the succeeding dialogue response generation. Our framework employs reinforcement learning with a dedicatedly designed critic network for reward judgement. Experimental results from automatic and human evaluations indicate that our framework is capable of generating relevant, interesting, coherent and informative partner personas, even compared to the ground truth partner personas. This enhances the succeeding dialogue response generation, which surpasses our competitive baselines that condition on the ground truth partner personas.

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Grounded Dialogue Generation with Cross-encoding Re-ranker, Grounding Span Prediction, and Passage Dropout
Kun Li | Tianhua Zhang | Liping Tang | Junan Li | Hongyuan Lu | Xixin Wu | Helen Meng
Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering

MultiDoc2Dial presents an important challenge on modeling dialogues grounded with multiple documents. This paper proposes a pipeline system of “retrieve, re-rank, and generate”, where each component is individually optimized. This enables the passage re-ranker and response generator to fully exploit training with ground-truth data. Furthermore, we use a deep cross-encoder trained with localized hard negative passages from the retriever. For the response generator, we use grounding span prediction as an auxiliary task to be jointly trained with the main task of response generation. We also adopt a passage dropout and regularization technique to improve response generation performance. Experimental results indicate that the system clearly surpasses the competitive baseline and our team CPII-NLP ranked 1st among the public submissions on ALL four leaderboards based on the sum of F1, SacreBLEU, METEOR and RougeL scores.

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On Controlling Fallback Responses for Grounded Dialogue Generation
Hongyuan Lu | Wai Lam | Hong Cheng | Helen Meng
Findings of the Association for Computational Linguistics: ACL 2022

Dialogue agents can leverage external textual knowledge to generate responses of a higher quality. To our best knowledge, most existing works on knowledge grounded dialogue settings assume that the user intention is always answerable. Unfortunately, this is impractical as there is no guarantee that the knowledge retrievers could always retrieve the desired knowledge. Therefore, this is crucial to incorporate fallback responses to respond to unanswerable contexts appropriately while responding to the answerable contexts in an informative manner. We propose a novel framework that automatically generates a control token with the generator to bias the succeeding response towards informativeness for answerable contexts and fallback for unanswerable contexts in an end-to-end manner. Since no existing knowledge grounded dialogue dataset considers this aim, we augment the existing dataset with unanswerable contexts to conduct our experiments. Automatic and human evaluation results indicate that naively incorporating fallback responses with controlled text generation still hurts informativeness for answerable context. In contrast, our proposed framework effectively mitigates this problem while still appropriately presenting fallback responses to unanswerable contexts. Such a framework also reduces the extra burden of the additional classifier and the overheads introduced in the previous works, which operates in a pipeline manner.


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Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings
Pengfei Liu | Shafiq Joty | Helen Meng
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Analysis of Dysarthric Speech using Distinctive Feature Recognition
Ka Ho Wong | Yu Ting Yeung | Patrick C. M. Wong | Gina-Anne Levow | Helen Meng
Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies


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SeemGo: Conditional Random Fields Labeling and Maximum Entropy Classification for Aspect Based Sentiment Analysis
Pengfei Liu | Helen Meng
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)


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Automatic Story Segmentation using a Bayesian Decision Framework for Statistical Models of Lexical Chain Features
Wai-Kit Lo | Wenying Xiong | Helen Meng
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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Developing Speech Recognition and Synthesis Technologies to Support Computer-Aided Pronunciation Training for Chinese Learners of English
Helen Meng
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1


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Combined Use of Speaker- and Tone-Normalized Pitch Reset with Pause Duration for Automatic Story Segmentation in Mandarin Broadcast News
Lei Xie | Chuan Liu | Helen Meng
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers


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A Maximum Entropy Framework that Integrates Word Dependencies and Grammatical Relations for Reading Comprehension
Kui Xu | Helen Meng | Fuliang Weng
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers


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The Use of Metadata, Web-derived Answer Patterns and Passage Context to Improve Reading Comprehension Performance
Yongping Du | Helen Meng | Xuanjing Huang | Lide Wu
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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Design and Development of a Bilingual Reading Comprehension Corpus
Kui Xu | Helen Meng
International Journal of Computational Linguistics & Chinese Language Processing, Volume 10, Number 2, June 2005: Special Issue on Annotated Speech Corpora


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Automatic Grammar Partitioning for Syntactic Parsing
Po Chui Luk | Fuliang Weng | Helen Meng
Proceedings of the Seventh International Workshop on Parsing Technologies

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Design, Compilation and Processing of CUCall: A Set of Cantonese Spoken Language Corpora Collected Over Telephone Networks
W.K. Lo | P.C. Ching | Tan Lee | Helen Meng
Proceedings of Research on Computational Linguistics Conference XIV

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Mandarin-English Information: Investigating Translingual Speech Retrieval
Helen Meng | Berlin Chen | Sanjeev Khudanpur | Gina-Anne Levow | Wai-Kit Lo | Douglas Oard | Patrick Shone | Karen Tang | Hsin-Min Wang | Jianqiang Wang
Proceedings of the First International Conference on Human Language Technology Research

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Scalability and Portability of a Belief Network-based Dialog Model for Different Application Domains
Carmen Wai | Helen M. Meng | Roberto Pieraccini
Proceedings of the First International Conference on Human Language Technology Research


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Mandarin-English Information (MEI): Investigating Translingual Speech Retrieval
Helen Meng | Sanjeev Khudanpur | Gina Levow | Douglas W. Oard | Hsin-Min Wang
ANLP-NAACL 2000 Workshop: Embedded Machine Translation Systems

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Parsing a Lattice with Multiple Grammars
Fuliang Weng | Helen Meng | Po Chui Luk
Proceedings of the Sixth International Workshop on Parsing Technologies

Efficiency, memory, ambiguity, robustness and scalability are the central issues in natural language parsing. Because of the complexity of natural language, different parsers may be suited only to certain subgrammars. In addition, grammar maintenance and updating may have adverse effects on tuned parsers. Motivated by these concerns, [25] proposed a grammar partitioning and top-down parser composition mechanism for loosely restricted Context-Free Grammars (CFGs). In this paper, we report on significant progress, i.e., (1) developing guidelines for the grammar partition through a set of heuristics, (2) devising a new mix-strategy composition algorithms for any rule-based grammar partition in a lattice framework, and 3) initial but encouraging parsing results for Chinese and English queries from an Air Travel Information System (ATIS) corpus.


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An Analytical Study of Transformational Tagging for Chinese Text
Helen Meng | Chun Wah Ip
ROCLING 1999 Short Papers


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Phonological Parsing for Bi-directional Letter-to-Sound/Sound-to-Letter Generation
Helen M. Meng | Stephanie Seneff | Victor W. Zue
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994


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Signal Representation Attribute Extraction and the Use Distinctive Features for Phonetic Classification
Helen M. Meng | Victor W. Zue | Hong C. Leung
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991