Junji Tomita


2020

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Unsupervised Domain Adaptation of Language Models for Reading Comprehension
Kosuke Nishida | Kyosuke Nishida | Itsumi Saito | Hisako Asano | Junji Tomita
Proceedings of the Twelfth Language Resources and Evaluation Conference

This study tackles unsupervised domain adaptation of reading comprehension (UDARC). Reading comprehension (RC) is a task to learn the capability for question answering with textual sources. State-of-the-art models on RC still do not have general linguistic intelligence; i.e., their accuracy worsens for out-domain datasets that are not used in the training. We hypothesize that this discrepancy is caused by a lack of the language modeling (LM) capability for the out-domain. The UDARC task allows models to use supervised RC training data in the source domain and only unlabeled passages in the target domain. To solve the UDARC problem, we provide two domain adaptation models. The first one learns the out-domain LM and in-domain RC task sequentially. The second one is the proposed model that uses a multi-task learning approach of LM and RC. The models can retain both the RC capability acquired from the supervised data in the source domain and the LM capability from the unlabeled data in the target domain. We evaluated the models on UDARC with five datasets in different domains. The models outperformed the model without domain adaptation. In particular, the proposed model yielded an improvement of 4.3/4.2 points in EM/F1 in an unseen biomedical domain.

2019

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Multi-style Generative Reading Comprehension
Kyosuke Nishida | Itsumi Saito | Kosuke Nishida | Kazutoshi Shinoda | Atsushi Otsuka | Hisako Asano | Junji Tomita
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

This study tackles generative reading comprehension (RC), which consists of answering questions based on textual evidence and natural language generation (NLG). We propose a multi-style abstractive summarization model for question answering, called Masque. The proposed model has two key characteristics. First, unlike most studies on RC that have focused on extracting an answer span from the provided passages, our model instead focuses on generating a summary from the question and multiple passages. This serves to cover various answer styles required for real-world applications. Second, whereas previous studies built a specific model for each answer style because of the difficulty of acquiring one general model, our approach learns multi-style answers within a model to improve the NLG capability for all styles involved. This also enables our model to give an answer in the target style. Experiments show that our model achieves state-of-the-art performance on the Q&A task and the Q&A + NLG task of MS MARCO 2.1 and the summary task of NarrativeQA. We observe that the transfer of the style-independent NLG capability to the target style is the key to its success.

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Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction
Kosuke Nishida | Kyosuke Nishida | Masaaki Nagata | Atsushi Otsuka | Itsumi Saito | Hisako Asano | Junji Tomita
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Question answering (QA) using textual sources for purposes such as reading comprehension (RC) has attracted much attention. This study focuses on the task of explainable multi-hop QA, which requires the system to return the answer with evidence sentences by reasoning and gathering disjoint pieces of the reference texts. It proposes the Query Focused Extractor (QFE) model for evidence extraction and uses multi-task learning with the QA model. QFE is inspired by extractive summarization models; compared with the existing method, which extracts each evidence sentence independently, it sequentially extracts evidence sentences by using an RNN with an attention mechanism on the question sentence. It enables QFE to consider the dependency among the evidence sentences and cover important information in the question sentence. Experimental results show that QFE with a simple RC baseline model achieves a state-of-the-art evidence extraction score on HotpotQA. Although designed for RC, it also achieves a state-of-the-art evidence extraction score on FEVER, which is a recognizing textual entailment task on a large textual database.

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A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension
Yasuhito Ohsugi | Itsumi Saito | Kyosuke Nishida | Hisako Asano | Junji Tomita
Proceedings of the First Workshop on NLP for Conversational AI

Conversational machine comprehension (CMC) requires understanding the context of multi-turn dialogue. Using BERT, a pretraining language model, has been successful for single-turn machine comprehension, while modeling multiple turns of question answering with BERT has not been established because BERT has a limit on the number and the length of input sequences. In this paper, we propose a simple but effective method with BERT for CMC. Our method uses BERT to encode a paragraph independently conditioned with each question and each answer in a multi-turn context. Then, the method predicts an answer on the basis of the paragraph representations encoded with BERT. The experiments with representative CMC datasets, QuAC and CoQA, show that our method outperformed recently published methods (+0.8 F1 on QuAC and +2.1 F1 on CoQA). In addition, we conducted a detailed analysis of the effects of the number and types of dialogue history on the accuracy of CMC, and we found that the gold answer history, which may not be given in an actual conversation, contributed to the model performance most on both datasets.

2018

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Commonsense Knowledge Base Completion and Generation
Itsumi Saito | Kyosuke Nishida | Hisako Asano | Junji Tomita
Proceedings of the 22nd Conference on Computational Natural Language Learning

This study focuses on acquisition of commonsense knowledge. A previous study proposed a commonsense knowledge base completion (CKB completion) method that predicts a confidence score of for triplet-style knowledge for improving the coverage of CKBs. To improve the accuracy of CKB completion and expand the size of CKBs, we formulate a new commonsense knowledge base generation task (CKB generation) and propose a joint learning method that incorporates both CKB completion and CKB generation. Experimental results show that the joint learning method improved completion accuracy and the generation model created reasonable knowledge. Our generation model could also be used to augment data and improve the accuracy of completion.

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Natural Language Inference with Definition Embedding Considering Context On the Fly
Kosuke Nishida | Kyosuke Nishida | Hisako Asano | Junji Tomita
Proceedings of the Third Workshop on Representation Learning for NLP

Natural language inference (NLI) is one of the most important tasks in NLP. In this study, we propose a novel method using word dictionaries, which are pairs of a word and its definition, as external knowledge. Our neural definition embedding mechanism encodes input sentences with the definitions of each word of the sentences on the fly. It can encode the definition of words considering the context of input sentences by using an attention mechanism. We evaluated our method using WordNet as a dictionary and confirmed that our method performed better than baseline models when using the full or a subset of 100d GloVe as word embeddings.

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Introduction method for argumentative dialogue using paired question-answering interchange about personality
Kazuki Sakai | Ryuichiro Higashinaka | Yuichiro Yoshikawa | Hiroshi Ishiguro | Junji Tomita
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

To provide a better discussion experience in current argumentative dialogue systems, it is necessary for the user to feel motivated to participate, even if the system already responds appropriately. In this paper, we propose a method that can smoothly introduce argumentative dialogue by inserting an initial discourse, consisting of question-answer pairs concerning personality. The system can induce interest of the users prior to agreement or disagreement during the main discourse. By disclosing their interests, the users will feel familiarity and motivation to further engage in the argumentative dialogue and understand the system’s intent. To verify the effectiveness of a question-answer dialogue inserted before the argument, a subjective experiment was conducted using a text chat interface. The results suggest that inserting the question-answer dialogue enhances familiarity and naturalness. Notably, the results suggest that women more than men regard the dialogue as more natural and the argument as deepened, following an exchange concerning personality.

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Role play-based question-answering by real users for building chatbots with consistent personalities
Ryuichiro Higashinaka | Masahiro Mizukami | Hidetoshi Kawabata | Emi Yamaguchi | Noritake Adachi | Junji Tomita
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

Having consistent personalities is important for chatbots if we want them to be believable. Typically, many question-answer pairs are prepared by hand for achieving consistent responses; however, the creation of such pairs is costly. In this study, our goal is to collect a large number of question-answer pairs for a particular character by using role play-based question-answering in which multiple users play the roles of certain characters and respond to questions by online users. Focusing on two famous characters, we conducted a large-scale experiment to collect question-answer pairs by using real users. We evaluated the effectiveness of role play-based question-answering and found that, by using our proposed method, the collected pairs lead to good-quality chatbots that exhibit consistent personalities.

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Predicting Nods by using Dialogue Acts in Dialogue
Ryo Ishii | Ryuichiro Higashinaka | Junji Tomita
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Creating Large-Scale Argumentation Structures for Dialogue Systems
Kazuki Sakai | Akari Inago | Ryuichiro Higashinaka | Yuichiro Yoshikawa | Hiroshi Ishiguro | Junji Tomita
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Automatically Extracting Variant-Normalization Pairs for Japanese Text Normalization
Itsumi Saito | Kyosuke Nishida | Kugatsu Sadamitsu | Kuniko Saito | Junji Tomita
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Social media texts, such as tweets from Twitter, contain many types of non-standard tokens, and the number of normalization approaches for handling such noisy text has been increasing. We present a method for automatically extracting pairs of a variant word and its normal form from unsegmented text on the basis of a pair-wise similarity approach. We incorporated the acquired variant-normalization pairs into Japanese morphological analysis. The experimental results show that our method can extract widely covered variants from large Twitter data and improve the recall of normalization without degrading the overall accuracy of Japanese morphological analysis.

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Improving Neural Text Normalization with Data Augmentation at Character- and Morphological Levels
Itsumi Saito | Jun Suzuki | Kyosuke Nishida | Kugatsu Sadamitsu | Satoshi Kobashikawa | Ryo Masumura | Yuji Matsumoto | Junji Tomita
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

In this study, we investigated the effectiveness of augmented data for encoder-decoder-based neural normalization models. Attention based encoder-decoder models are greatly effective in generating many natural languages. % such as machine translation or machine summarization. In general, we have to prepare for a large amount of training data to train an encoder-decoder model. Unlike machine translation, there are few training data for text-normalization tasks. In this paper, we propose two methods for generating augmented data. The experimental results with Japanese dialect normalization indicate that our methods are effective for an encoder-decoder model and achieve higher BLEU score than that of baselines. We also investigated the oracle performance and revealed that there is sufficient room for improving an encoder-decoder model.

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Investigating the Effect of Conveying Understanding Results in Chat-Oriented Dialogue Systems
Koh Mitsuda | Ryuichiro Higashinaka | Junji Tomita
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

In dialogue systems, conveying understanding results of user utterances is important because it enables users to feel understood by the system. However, it is not clear what types of understanding results should be conveyed to users; some utterances may be offensive and some may be too commonsensical. In this paper, we explored the effect of conveying understanding results of user utterances in a chat-oriented dialogue system by an experiment using human subjects. As a result, we found that only certain types of understanding results, such as those related to a user’s permanent state, are effective to improve user satisfaction. This paper clarifies the types of understanding results that can be safely uttered by a system.

2006

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Expanding the Recall of Relation Extraction by Bootstrapping
Junji Tomita | Stephen Soderland | Oren Etzioni
Proceedings of the Workshop on Adaptive Text Extraction and Mining (ATEM 2006)