Journal of Natural Language Processing
Online ISSN : 2185-8314
Print ISSN : 1340-7619
ISSN-L : 1340-7619
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Displaying 1-16 of 16 articles from this issue
Preface (Non Peer-Reviewed)
General Paper (Peer-Reviewed)
  • Ying Zhang, Hidetaka Kamigaito, Manabu Okumura
    2024 Volume 31 Issue 1 Pages 3-46
    Published: 2024
    Released on J-STAGE: March 15, 2024
    JOURNAL FREE ACCESS

    Pre-trained sequence-to-sequence (seq2seq) models have achieved state-of-the-art results in the grammatical error correction tasks. However, these models are plagued by prediction bias owing to their unidirectional decoding. Thus, this study proposed a bidirectional transformer reranker (BTR) that re-estimates the probability of each candidate sentence generated by the pre-trained seq2seq model. The BTR preserves the seq2seq-style transformer architecture but utilizes a BERT-style self-attention mechanism in the decoder to compute the probability of each target token using masked language modeling to capture bidirectional representations from the target context. To guide the reranking process, the BTR adopted negative sampling in the objective function to minimize the unlikelihood. During inference, the BTR yielded the final results after comparing the reranked top-1 results with the original ones using an acceptance threshold λ. Experimental results showed that, when reranking candidates from a pre-trained seq2seq model, the T5-base, the BTR on top of T5-base yielded scores of 65.47 and 71.27 F0.5 on the CoNLL-14 and building educational applications 2019 (BEA) test sets, respectively, and yielded 59.52 GLEU score on the JFLEG corpus, with improvements of 0.36, 0.76, and 0.48 points compared with the original T5-base. Furthermore, when reranking candidates from T5-large, the BTR on top of T5-base improved the original T5-large by 0.26 on the BEA test set.

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  • Tomoki Ariyama, Jun Suzuki, Masatoshi Suzuki, Ryota Tanaka, Reina Akam ...
    2024 Volume 31 Issue 1 Pages 47-78
    Published: 2024
    Released on J-STAGE: March 15, 2024
    JOURNAL FREE ACCESS

    Question answering(QA) is one of the most crucial research topics within the realm of Natural Language Processing. Recent advances in deep learning methodologies, coupled with the burgeoning availability of linguistic resources, have led to dramatic progresses in QA. However, most of these studies have been conducted in English, and only a little research has been done on QA in Japanese. To encourage research endeavors in Japanese QA, we organized three quiz competitions, encompassing questions posed in Japanese. This study undertook an analysis of the trivia questions employed in these competitions, scrutinized the QA systems submitted, and introduced comparisons with large language models to unveil the current achievements and challenges within the domain of QA in Japanese. This paper presents the findings of the analysis.

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  • Yasumasa Kano, Katsuhito Sudoh, Satoshi Nakamura
    2024 Volume 31 Issue 1 Pages 79-104
    Published: 2024
    Released on J-STAGE: March 15, 2024
    JOURNAL FREE ACCESS

    Simultaneous translation is a task that starts translation even before the speaker has finished speaking. This study focuses on prefix-to-prefix translation and proposes a method to align prefixes in a bilingual sentence pair iteratively to train a machine translation model to work with prefix-to-prefix. In the experiments, the proposed method demonstrated higher BLEU than those of the baseline methods in low latency ranges on the IWSLT simultaneous translation benchmark. However, the proposed method degraded the performance in high latency ranges in the English-to-Japanese experiments; thus, we analyzed it in length ratios and prefix boundary prediction accuracies. The obtained results suggested that the degraded performance was due to the large word order difference between English and Japanese.

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  • Youmi Ma, An Wang, Naoaki Okazaki
    2024 Volume 31 Issue 1 Pages 105-133
    Published: 2024
    Released on J-STAGE: March 15, 2024
    JOURNAL FREE ACCESS

    Document-level relation extraction (DocRE) is the task of identifying all relations between each entity pair in a document. Evidence, a set of sentences containing enough clues for deciding the relation between an entity pair, has been shown to benefit relation extraction. Previous works tackle Evidence Retrieval (ER) and DocRE as separate tasks, while this work propose to incorporate ER directly into the DocRE model. Specifically, we guide the self attention mechanism to assign higher weights on evidence when encoding entity pairs. In this way we obtain contextualized representations focused on evidence. We further propose to learn ER on massive data without evidence annotations from automatically-generated evidence. Experimental results show that our approach exhibits state-of-the-art performance on DocRED and Re-DocRED, two popular benchmarks for DocRE, in both DocRE and ER.

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  • Hao Wang, Hirofumi Shimizu, Daisuke Kawahara
    2024 Volume 31 Issue 1 Pages 134-154
    Published: 2024
    Released on J-STAGE: March 15, 2024
    JOURNAL FREE ACCESS

    Recent studies in natural language processing (NLP) have focused on modern languages and achieved state-of-the-art results in several tasks. Meanwhile, little attention has been paid to ancient texts and related tasks. Classical Chinese first came to Japan approximately 2,000 years ago. It was gradually adapted to a Japanese form called Kanbun-Kundoku (Kanbun) in Japanese reading and translating methods that have significantly impacted Japanese literature. However, compared with the abundant resources for ancient texts in mainland China, Kanbun resources remain scarce in Japan. Therefore, we construct a parallel Classical-Chinese-to-Kanbun dataset consisting of the Tang poetry. Furthermore, we introduce two tasks, character reordering and machine translation, both of which play a significant role in Kanbun comprehension. We also test the current language models on these tasks and discuss the best evaluation method by comparing the results with human scores. We release our code and dataset on GitHub: https://github.com/nlp-waseda/Kanbun-LM.

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  • Haiyue Song, Zhuoyuan Mao, Raj Dabre, Chenhui Chu, Sadao Kurohashi
    2024 Volume 31 Issue 1 Pages 155-188
    Published: 2024
    Released on J-STAGE: March 15, 2024
    JOURNAL FREE ACCESS

    In this study, we proposed DiverSeg to exploit diverse segmentations from multiple subword segmenters that capture the various perspectives of each word for neural machine translation. In DiverSeg, multiple segmentations are encoded using a subword lattice input, a subword-relation-aware attention mechanism integrates relations among subwords, and a cross-granularity embedding alignment objective enhances the similarity across different segmentations of a word. We conducted experiments on five datasets to evaluate the effectiveness of DiverSeg in improving machine translation quality. The results demonstrate that DiverSeg outperforms baseline methods by approximately two BLEU points. Additionally, we performed ablation studies to investigate the improvement over non-subword methods, the contribution of each component of DiverSeg, the choice of subword relations, the choice of similarity metrics in alignment loss, and combinations of segmenters.

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  • Jian Wu, Yicheng Xu, Börje F. Karlsson, Manabu Okumura
    2024 Volume 31 Issue 1 Pages 189-211
    Published: 2024
    Released on J-STAGE: March 15, 2024
    JOURNAL FREE ACCESS

    Hybrid Question-Answering (HQA), which targets reasoning over tables and passages linked from table cells, has witnessed significant research in recent years. A common challenge in HQA and other passage-table QA datasets is that it is generally unrealistic to iterate over all table rows, columns, and linked passages to retrieve evidence. Such a challenge made it difficult for previous studies to show their reasoning ability in retrieving answers. To bridge this gap, we propose a novel Table-alignment-based Cell-selection and Reasoning model (TACR) for hybrid text and table QA, evaluated on the HybridQA and WikiTableQuestions datasets. In evidence retrieval, we design a table-question-alignment enhanced cell-selection method to retrieve fine-grained evidence. In answer reasoning, we incorporate a QA module that treats the row containing selected cells as context. Experimental results over the HybridQA and WikiTableQuestions (WTQ) datasets show that TACR achieves state-of-the-art results on cell selection and outperforms fine-grained evidence retrieval baselines on HybridQA, while achieving competitive performance on WTQ. We also conducted a detailed analysis to demonstrate that being able to align questions to tables in the cell-selection stage can result in important gains from experiments of over 90% table row and column selection accuracy, meanwhile also improving output explainability.

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  • Koichiro Ito, Masaki Murata, Tomohiro Ohno, Shigeki Matsubara
    2024 Volume 31 Issue 1 Pages 212-249
    Published: 2024
    Released on J-STAGE: March 15, 2024
    JOURNAL FREE ACCESS

    Conversational agents, such as communication robots, are expected to play a role in listening to narratives. To recognize these robots as the listeners, it is essential for them to have a function that indicates their attentive listening to a narrative. The basic response strategy in attentive listening involves generating responsive utterances that show acceptance. However, the narratives occasionally contain self-deprecation or modesty. In such cases, the listener must be able to produce a response that shows disagreement with the narrator's utterance, that is, a disagreement response. This study demonstrates the feasibility of generating appropriate disagreement responses. First, we define a method to tag the timing and expression of the disagreement response to narrative data in a non-real-time environment, and verify that the method enables to construct a corpus to which the disagreement response timing and response expression are comprehensively and stably assigned, respectively. Next, we implement detection and classification methods for disagreement response timing and expressions, respectively, based on a pre-trained transformer-based model and verify the feasibility of generating disagreement responses using the response corpus through experiments.

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  • Congda Ma, Tianyu Zhao, Makoto Shing, Kei Sawada, Manabu Okumura
    2024 Volume 31 Issue 1 Pages 250-265
    Published: 2024
    Released on J-STAGE: March 15, 2024
    JOURNAL FREE ACCESS

    In a controllable text generation dataset, unannotated attributes may provide irrelevant learning signals to models that use them for training, thereby degrading their performance. We propose focused prefix tuning(FPT) to mitigate this problem and enable control to focus on the desired attribute. Experimental results show that FPT can achieve better control accuracy and text fluency than baseline models in single-attribute control tasks. In multi-attribute control tasks, FPT achieves control accuracy comparable to that of the state-of-the-art approach while maintaining the flexibility to control new attributes without retraining existing models.

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