Daling Wang


2019

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Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation
Weichao Wang | Shi Feng | Daling Wang | Yifei Zhang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Generating intriguing question is a key step towards building human-like open-domain chatbots. Although some recent works have focused on this task, compared with questions raised by humans, significant gaps remain in maintaining semantic coherence with post, which may result in generating dull or deviated questions. We observe that the answer has strong semantic coherence to its question and post, which can be used to guide question generation. Thus, we devise two methods to further enhance semantic coherence between post and question under the guidance of answer. First, the coherence score between generated question and answer is used as the reward function in a reinforcement learning framework, to encourage the cases that are consistent with the answer in semantic. Second, we incorporate adversarial training to explicitly control question generation in the direction of question-answer coherence. Extensive experiments show that our two methods outperform state-of-the-art baseline algorithms with large margins in raising semantic coherent questions.

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Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension
Qian Li | Hui Su | Cheng Niu | Daling Wang | Zekang Li | Shi Feng | Yifei Zhang
Proceedings of the 2nd Workshop on Machine Reading for Question Answering

In conversational machine comprehension, it has become one of the research hotspots integrating conversational history information through question reformulation for obtaining better answers. However, the existing question reformulation models are trained only using supervised question labels annotated by annotators without considering any feedback information from answers. In this paper, we propose a novel Answer-Supervised Question Reformulation (ASQR) model for enhancing conversational machine comprehension with reinforcement learning technology. ASQR utilizes a pointer-copy-based question reformulation model as an agent, takes an action to predict the next word, and observes a reward for the whole sentence state after generating the end-of-sequence token. The experimental results on QuAC dataset prove that our ASQR model is more effective in conversational machine comprehension. Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset.

2018

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Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning
Weichao Wang | Shi Feng | Wei Gao | Daling Wang | Yifei Zhang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Sentiment expression in microblog posts can be affected by user’s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user’s language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel adversarial cross-lingual learning framework, in which with the help of user properties as bridge between languages, we can extract the language-specific features and language-independent features to enrich the user post representation so as to alleviate the data insufficiency problem. Results on English and Chinese microblog datasets confirm that our method outperforms state-of-the-art baseline algorithms with large margins.

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A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness
Xiangju Li | Kaisong Song | Shi Feng | Daling Wang | Yifei Zhang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Emotion cause analysis has been a key topic in natural language processing. Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue. Meanwhile, the clauses in a document play different roles on stimulating a certain emotion, depending on their content relevance. Therefore, we propose a co-attention neural network model for emotion cause analysis with emotional context awareness. The method encodes the clauses with a co-attention based bi-directional long short-term memory into high-level input representations, which are further fed into a convolutional layer for emotion cause analysis. Experimental results show that our approach outperforms the state-of-the-art baseline methods.

2015

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NDMSCS: A Topic-Based Chinese Microblog Polarity Classification System
Yang Wang | Yaqi Wang | Shi Feng | Daling Wang | Yifei Zhang
Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing

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NEUDM: A System for Topic-Based Message Polarity Classification
Yaqi Wang | Shi Feng | Daling Wang | Yifei Zhang
Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing

2013

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Is Twitter A Better Corpus for Measuring Sentiment Similarity?
Shi Feng | Le Zhang | Binyang Li | Daling Wang | Ge Yu | Kam-Fai Wong
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing