2021
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Entity Resolution in Open-domain Conversations
Mingyue Shang
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Tong Wang
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Mihail Eric
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Jiangning Chen
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Jiyang Wang
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Matthew Welch
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Tiantong Deng
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Akshay Grewal
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Han Wang
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Yue Liu
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Yang Liu
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Dilek Hakkani-Tur
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers
In recent years, incorporating external knowledge for response generation in open-domain conversation systems has attracted great interest. To improve the relevancy of retrieved knowledge, we propose a neural entity linking (NEL) approach. Different from formal documents, such as news, conversational utterances are informal and multi-turn, which makes it more challenging to disambiguate the entities. Therefore, we present a context-aware named entity recognition model (NER) and entity resolution (ER) model to utilize dialogue context information. We conduct NEL experiments on three open-domain conversation datasets and validate that incorporating context information improves the performance of NER and ER models. The end-to-end NEL approach outperforms the baseline by 62.8% relatively in F1 metric. Furthermore, we verify that using external knowledge based on NEL benefits the neural response generation model.
2019
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Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations
Ran Le
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Wenpeng Hu
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Mingyue Shang
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Zhenjun You
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Lidong Bing
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Dongyan Zhao
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Rui Yan
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario. In real multi- party conversations, we can observe who is speaking, but the addressee information is not always explicit. In this paper, we aim to tackle the challenge of identifying all the miss- ing addressees in a conversation session. To this end, we introduce a novel who-to-whom (W2W) model which models users and utterances in the session jointly in an interactive way. We conduct experiments on the benchmark Ubuntu Multi-Party Conversation Corpus and the experimental results demonstrate that our model outperforms baselines with consistent improvements.
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Semi-supervised Text Style Transfer: Cross Projection in Latent Space
Mingyue Shang
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Piji Li
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Zhenxin Fu
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Lidong Bing
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Dongyan Zhao
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Shuming Shi
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Rui Yan
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data. In this paper, we first propose a semi-supervised text style transfer model that combines the small-scale parallel data with the large-scale nonparallel data. With these two types of training data, we introduce a projection function between the latent space of different styles and design two constraints to train it. We also introduce two other simple but effective semi-supervised methods to compare with. To evaluate the performance of the proposed methods, we build and release a novel style transfer dataset that alters sentences between the style of ancient Chinese poem and the modern Chinese.