Papers by Anjie Fang
Reinforced Question Rewriting for Conversational Question Answering (2022.emnlp-industry)
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| Challenge: | Existing approaches to CQA involve training new models from scratch . existing approaches are expensive and often not feasible . |
| Approach: | They propose to use QA feedback to supervise the rewriting model with reinforcement learning. |
| Outcome: | The proposed model can improve QA performance over baselines for extractive and retrieval QA. |
Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition (2022.naacl-main)
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| Challenge: | Named entity recognition (NER) models trained on CoNLL do not transfer well to other domains, even within the same language. |
| Approach: | They propose a token-level gating layer to augment pre-trained multilingual transformers with gazetteers containing named entities (NE) from a target language or domain. |
| Outcome: | The proposed model improves on cross-lingual transfer with an F1 score of 92.92 for English and an average of 89.43 across all languages in CoNLL. |
MultiCoNER: A Large-scale Multilingual Dataset for Complex Named Entity Recognition (2022.coling-1)
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| Challenge: | Named Entity Recognition (NER) is a core task in Natural Language Processing. |
| Approach: | They present a large multilingual dataset for Named Entity Recognition that covers 3 domains across 11 languages and multilingual and code-mixing subsets. |
| Outcome: | The proposed dataset is large and multilingual, covering 11 languages and subsets. |
GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input (2021.naacl-main)
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| Challenge: | Named Entity Recognition (NER) is difficult in real-world settings due to short texts, emerging entities, and complex entities. |
| Approach: | They propose a flexible Gazetteer Representation encoder and a Mixture-of-Experts gating network for gazetteer knowledge integration. |
| Outcome: | The proposed approach shows large gains (up to +49% F1) in recognizing difficult entities compared to baselines. |
CycleKQR: Unsupervised Bidirectional Keyword-Question Rewriting (2022.emnlp-main)
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| Challenge: | Existing approaches to query paraphrases are based on encoderdecoder architectures, but they do not support the two important functionalities beyond questions. |
| Approach: | They propose a keyword-question rewriting task to improve query understanding capabilities of NLU systems for all surface forms. |
| Outcome: | Empirically, we show that CycleKQR significantly improves QA performance by rewriting queries into the appropriate form while retaining the original semantic meaning of input queries. |
Follow-on Question Suggestion via Voice Hints for Voice Assistants (2023.findings-emnlp)
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| Challenge: | Query suggestion is a standard feature of screen-based search experiences, but it is not trivial to implement in voice-based settings. |
| Approach: | They propose a task of suggesting questions with compact voice hints to allow users to ask follow-up questions. |
| Outcome: | The proposed approach is based on a dataset of 6681 input questions and human written hints and is highly linguistically motivated. |