A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems

Neural network models usually suffer from the challenge of incorporating commonsense knowledge into the open-domain dialogue systems. In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation. In addition, we propose a response guiding attention and a multi-step decoding strategy to steer our model to focus on relevant features for response generation. Experiments on two benchmark datasets demonstrate that our model has robust superiority over compared methods in generating informative and fluent dialogues. Our code is available at https://github.com/siat-nlp/TransDG.

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GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems

10.18653/v1/2020.emnlp-main.742 ◽

2020 ◽

Author(s):

Lishan Huang ◽

Zheng Ye ◽

Jinghui Qin ◽

Liang Lin ◽

Xiaodan Liang

Keyword(s):

Dialogue Systems ◽

Open Domain

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MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering

Transactions of the Association for Computational Linguistics ◽

10.1162/tacl_a_00433 ◽

2021 ◽

Vol 9 ◽

pp. 1389-1406

Author(s):

Shayne Longpre ◽

Yi Lu ◽

Joachim Daiber

Keyword(s):

Question Answering ◽

State Of The Art ◽

Linguistically Diverse ◽

Data Representation ◽

Independent Data ◽

Open Domain ◽

Low Resource ◽

Art Methods ◽

Questions And Answers ◽

Cross Lingual

Abstract Progress in cross-lingual modeling depends on challenging, realistic, and diverse evaluation sets. We introduce Multilingual Knowledge Questions and Answers (MKQA), an open- domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). Answers are based on heavily curated, language- independent data representation, making results comparable across languages and independent of language-specific passages. With 26 languages, this dataset supplies the widest range of languages to-date for evaluating question answering. We benchmark a variety of state- of-the-art methods and baselines for generative and extractive question answering, trained on Natural Questions, in zero shot and translation settings. Results indicate this dataset is challenging even in English, but especially in low-resource languages.1

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