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Qiyu Ren

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AAAI Conference 2021 Conference Paper

Dynamic Hybrid Relation Exploration Network for Cross-Domain Context-Dependent Semantic Parsing

Binyuan Hui
Ruiying Geng
Qiyu Ren
Binhua Li
Yongbin Li
Jian Sun
Fei Huang
Luo Si

Semantic parsing has long been a fundamental problem in natural language processing. Recently, cross-domain contextdependent semantic parsing has become a new focus of research. Central to the problem is the challenge of leveraging contextual information of both natural language utterance and database schemas in the interaction history. In this paper, we present a dynamic graph framework that is capable of effectively modelling contextual utterances, tokens, database schemas, and their complicated interaction as the conversation proceeds. The framework employs a dynamic memory decay mechanism that incorporates inductive bias to integrate enriched contextual relation representation, which is further enhanced with a powerful reranking model. At the time of writing, we demonstrate that the proposed framework outperforms all existing models by large margins, achieving new state-of-the-art performance on two large-scale benchmarks, the SParC and CoSQL datasets. Specifically, the model attains a 55. 8% question-match and 30. 8% interaction-match accuracy on SParC, and a 46. 8% question-match and 17. 0% interaction-match accuracy on CoSQL.

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AAAI Conference 2020 Conference Paper

Multi-Task Learning with Generative Adversarial Training for Multi-Passage Machine Reading Comprehension

Qiyu Ren
Xiang Cheng
Sen Su

Multi-passage machine reading comprehension (MRC) aims to answer a question by multiple passages. Existing multipassage MRC approaches have shown that employing passages with and without golden answers (i. e. labeled and unlabeled passages) for model training can improve prediction accuracy. In this paper, we present MG-MRC, a novel approach for multi-passage MRC via multi-task learning with generative adversarial training. MG-MRC adopts the extract-thenselect framework, where an extractor is ﬁrst used to predict answer candidates, then a selector is used to choose the ﬁnal answer. In MG-MRC, we adopt multi-task learning to train the extractor by using both labeled and unlabeled passages. In particular, we use labeled passages to train the extractor by supervised learning, while using unlabeled passages to train the extractor by generative adversarial training, where the extractor is regarded as the generator and a discriminator is introduced to evaluate the generated answer candidates. Moreover, to train the extractor by backpropagation in the generative adversarial training process, we propose a hybrid method which combines boundary-based and content-based extracting methods to produce the answer candidate set and its representation. The experimental results on three open-domain QA datasets conﬁrm the effectiveness of our approach.

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