Papers by Yanyan Zou
Learning Cross-lingual Distributed Logical Representations for Semantic Parsing (P18-2)
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| Challenge: | Recent research efforts have looked into the problem of learning semantic parsers in a multilingual setup, but how to improve the performance of a monolingual semantic parsed system remains a research question that is under-explored. |
| Approach: | They propose to use data annotated in different languages to learn distributed representations of logical forms for improving a monolingual semantic parser. |
| Outcome: | The proposed method improves on the standard multilingual GeoQuery dataset. |
Pre-training for Abstractive Document Summarization by Reinstating Source Text (2020.emnlp-main)
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| Challenge: | Abstractive document summarization models are often trained on limited supervised data . authors present three objectives for pretraining abstractive summarizing models . |
| Approach: | They propose to pre-train a SEQ2SEQ based abstractive summarization model on unlabeled text. |
| Outcome: | The proposed method improves on two benchmark summarization datasets with 19GB of text . the goal is sentence reordering, next sentence generation and masked document generation . |
Text2Math: End-to-end Parsing Text into Math Expressions (D19-1)
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| Challenge: | Empirical results on benchmark datasets demonstrate the efficacy of our approach. |
| Approach: | They propose a model for semantically parsing text into math expressions and propose 'text2math' which aims to predict the complete math expression as a tree structure, with minimal manual efforts. |
| Outcome: | Empirical results on benchmark datasets demonstrate the efficacy of the proposed model. |
Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems (P19-1)
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| Challenge: | Existing methods to solve arithmetic word problems require additional annotations. |
| Approach: | They propose a method that automatically discovers hidden mathematical relations by tagging each quantity with a sign corresponding to one type of mathematical operation. |
| Outcome: | Empirical results show that the proposed method achieves 5 and 8 points of accuracy gains on two datasets compared to prior approaches. |
Incorporating Fine-grained Events in Stock Movement Prediction (D19-51)
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| Challenge: | Existing studies mainly adopt coarse-grained events, which loses the specific semantic information of diverse event types. |
| Approach: | They propose to use a finance event dictionary to extract fine-grained events from finance news to train a neural model that uses the extracted events as the distant supervised label to train stock prediction. |
| Outcome: | The proposed method outperforms baselines and has good generalizability. |
Summarizing Dialogues with Negative Cues (2022.coling-1)
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| Challenge: | Abstractive dialogue summarization aims to convert long dialogue content into its short form where the salient information is preserved while the redundant pieces are ignored. |
| Approach: | They propose to have the model perceive the redundant parts of an input dialogue history during the training phase. |
| Outcome: | The proposed method significantly outperforms baselines on the semantic matching and factual consistent based metrics. |
Automatic Scene-based Topic Channel Construction System for E-Commerce (2022.emnlp-industry)
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| Challenge: | Recent scene marketing has proved effective for offline shopping. |
| Approach: | They propose a novel product form, scene-based topic channel, which consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words. |
| Outcome: | The proposed system can be automated and tested on a real-world e-commerce recommendation platform. |
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. |
Joint Detection and Location of English Puns (N19-1)
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| Challenge: | Existing research on puns has focused on understanding the meanings of words and phrases. |
| Approach: | They propose a model that addresses pun detection and pun location jointly from a sequence labeling perspective. |
| Outcome: | Empirical results show that the proposed model can handle both homographic and heterographic puns. |
Aligning Cross-Lingual Entities with Multi-Aspect Information (D19-1)
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| Challenge: | Existing knowledge graphs that represent entities in different languages are not covered by existing systems. |
| Approach: | They propose two ways to embed entities from multilingual knowledge graphs into the same vector space, where equivalent entities are close to each other. |
| Outcome: | The proposed method significantly outperforms existing systems on two benchmark datasets. |
Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization (2021.findings-emnlp)
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| Challenge: | Existing methods to abstractly summarize dialogues are limited to two or more interlocutors. |
| Approach: | They propose to use existing document summarization models to capture the various topic information of a conversation and outline salient facts for the captured topics. |
| Outcome: | The proposed method significantly outperforms baselines and achieves new state-of-the-art performance on benchmark datasets. |