Papers by Yanyan Lan
Transductive Learning for Unsupervised Text Style Transfer (2021.emnlp-main)
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| Challenge: | Existing methods for style transfer are based on an inductive learning approach, which represents the style as embeddings, decoder parameters, or discriminator parameters and directly applies these general rules to the test cases. |
| Approach: | They propose a retrieval-based context-aware style representation that involves top-K relevant sentences in the target style in the transfer process. |
| Outcome: | The proposed method outperforms several strong baselines and is general and effective to the task of unsupervised style transfer. |
Augmenting Knowledge-grounded Conversations with Sequential Knowledge Transition (2021.naacl-main)
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| Challenge: | Existing knowledge-grounded dialogue models lack fine-grained control over knowledge selection and integration with dialogues. |
| Approach: | They propose to explicitly model the knowledge transition in sequential multi-turn conversations by abstracting knowledge into topic tags. |
| Outcome: | The proposed model outperforms baseline models on knowledge-grounded dialogue benchmarks. |
Tailored Sequence to Sequence Models to Different Conversation Scenarios (P18-1)
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| Challenge: | Sequence to sequence (Seq2Sequeq) models fail to meet the diverse requirements for different conversation scenarios, such as customer service and chatbot. |
| Approach: | They propose two optimized criteria for Sequence to sequence (Seq2Sequeq) to meet different conversation scenarios, i.e., maximum generated likelihood for specific-requirement scenario, and conditional value-at-risk for diverse-requrement scenarios. |
| Outcome: | The proposed models satisfies diverse requirements for different conversation scenarios and yields better performances than existing models. |
ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation (P19-1)
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| Challenge: | Existing hierarchical recurrent encoder-decoder models treat all contexts indiscriminately, which may hurt the following response generation process. |
| Approach: | They propose a hierarchical recurrent encoder-decoder model that treats all contexts indiscriminately and uses a word level LSTM encoder to obtain the initial representation of each context. |
| Outcome: | The proposed model outperforms baseline models on Chinese customer services and English Ubuntu dialogue datasets in terms of both metric-based and human evaluations. |
FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning (2021.findings-emnlp)
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| Challenge: | Existing neural dialogue models only capture syntactic and semantic information, but fail to model the logical consistency between the dialogue history and the generated response. |
| Approach: | They propose a fine-grained comparison model to capture syntactic and semantic information and then compare each candidate's representation with the whole history to obtain a history consistency representation. |
| Outcome: | The proposed model obtains higher ranking scores than baseline models on two public dialogue datasets. |
Adaptive Bridge between Training and Inference for Dialogue Generation (2021.emnlp-main)
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| Challenge: | Experimental results show that our model can achieve a significant improvement in terms of metric-based evaluation and human evaluation compared with the state-of-the-art exposure bias approaches. |
| Approach: | They propose a novel adaptive switching mechanism which automatically transits between ground-truth learning and generated learning regarding the word-level matching score. |
| Outcome: | The proposed model improves on Chinese and English reddit datasets compared with state-of-the-art models on the word-level matching score. |
Learning to Control the Specificity in Neural Response Generation (P18-1)
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| Challenge: | Existing generative conversational models tend to favor general and trivial responses which appear frequently. |
| Approach: | They propose a controlled response generation mechanism to handle different utterance-response relationships in terms of specificity. |
| Outcome: | The proposed model outperforms state-of-the-art models under automatic and human evaluations. |
Beyond Language: Learning Commonsense from Images for Reasoning (2020.findings-emnlp)
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| Challenge: | Existing commonsense reasoning methods use raw texts to perform data representation and answer prediction tasks. |
| Approach: | They propose a novel approach to learn commonsense from images instead of limited raw texts or costly knowledge bases. |
| Outcome: | The proposed approach outperforms language-based methods on commonsense reasoning problems on two commonsence reasoning problems. |
Adaptive Information Seeking for Open-Domain Question Answering (2021.emnlp-main)
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| Challenge: | Existing iterative approaches to open-domain question answering use predefined strategies . e.g., BM25, DPR, and hyperlink are defined as actions . |
| Approach: | They propose a novel adaptive information-seeking strategy for open-domain question answering . they propose to use a partially observed Markov decision process to select a proper retrieval action . |
| Outcome: | Experiments on SQuAD Open and HotpotQA fullwiki show that AISO outperforms baseline methods with predefined strategies in retrieval and answer evaluations. |
From spoken dialogue to formal summary: An utterance rewriting for dialogue summarization (2022.naacl-main)
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| Challenge: | Existing models focus more on the structure of summary, not on the personal and logical inconsistency problem. |
| Approach: | They propose a model to solve the problem of personal and logical inconsistency . they use an utterance rewriter to complete the ellipsis content of dialogue content . |
| Outcome: | The proposed model outperforms baseline models on both SAMSum and DialSum datasets. |