Yiran Wang


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24-bit Languages
Yiran Wang | Taro Watanabe | Masao Utiyama | Yuji Matsumoto
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

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Contextual Interaction for Argument Post Quality Assessment
Yiran Wang | Xuanang Chen | Ben He | Le Sun
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Recently, there has been an increased emphasis on assessing the quality of natural language arguments. Existing approaches primarily focus on evaluating the quality of individual argument posts. However, they often fall short when it comes to effectively distinguishing arguments that possess a narrow quality margin. To address this limitation, this paper delves into two alternative methods for modeling the relative quality of different arguments. These approaches include: 1) Supervised contrastive learning that captures the intricate interactions between arguments. By incorporating this approach, we aim to enhance the assessment of argument quality by effectively distinguishing between arguments with subtle differences in quality. 2) Large language models (LLMs) with in-context examples that harness the power of LLMs and enrich them with in-context examples. Through extensive evaluation and analysis on the publicly available IBM-Rank-30k dataset, we demonstrate the superiority of our contrastive argument quality assessment approach over state-of-the-art baselines. On the other hand, while LLMs with in-context examples showcase a commendable ability to identify high-quality argument posts, they exhibit relatively limited efficacy in discerning between argument posts with a narrow quality gap.


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What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation
Zuchao Li | Yiran Wang | Masao Utiyama | Eiichiro Sumita | Hai Zhao | Taro Watanabe
Findings of the Association for Computational Linguistics: ACL 2022

Deep learning has demonstrated performance advantages in a wide range of natural language processing tasks, including neural machine translation (NMT). Transformer NMT models are typically strengthened by deeper encoder layers, but deepening their decoder layers usually results in failure. In this paper, we first identify the cause of the failure of the deep decoder in the Transformer model. Inspired by this discovery, we then propose approaches to improving it, with respect to model structure and model training, to make the deep decoder practical in NMT. Specifically, with respect to model structure, we propose a cross-attention drop mechanism to allow the decoder layers to perform their own different roles, to reduce the difficulty of deep-decoder learning. For model training, we propose a collapse reducing training approach to improve the stability and effectiveness of deep-decoder training. We experimentally evaluated our proposed Transformer NMT model structure modification and novel training methods on several popular machine translation benchmarks. The results showed that deepening the NMT model by increasing the number of decoder layers successfully prevented the deepened decoder from degrading to an unconditional language model. In contrast to prior work on deepening an NMT model on the encoder, our method can deepen the model on both the encoder and decoder at the same time, resulting in a deeper model and improved performance.


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Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path
Yiran Wang | Hiroyuki Shindo | Yuji Matsumoto | Taro Watanabe
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

This paper presents a novel method for nested named entity recognition. As a layered method, our method extends the prior second-best path recognition method by explicitly excluding the influence of the best path. Our method maintains a set of hidden states at each time step and selectively leverages them to build a different potential function for recognition at each level. In addition, we demonstrate that recognizing innermost entities first results in better performance than the conventional outermost entities first scheme. We provide extensive experimental results on ACE2004, ACE2005, and GENIA datasets to show the effectiveness and efficiency of our proposed method.

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Structured Refinement for Sequential Labeling
Yiran Wang | Hiroyuki Shindo | Yuji Matsumoto | Taro Watanabe
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Kingsoft’s Neural Machine Translation System for WMT19
Xinze Guo | Chang Liu | Xiaolong Li | Yiran Wang | Guoliang Li | Feng Wang | Zhitao Xu | Liuyi Yang | Li Ma | Changliang Li
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper describes the Kingsoft AI Lab’s submission to the WMT2019 news translation shared task. We participated in two language directions: English-Chinese and Chinese-English. For both language directions, we trained several variants of Transformer models using the provided parallel data enlarged with a large quantity of back-translated monolingual data. The best translation result was obtained with ensemble and reranking techniques. According to automatic metrics (BLEU) our Chinese-English system reached the second highest score, and our English-Chinese system reached the second highest score for this subtask.


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Automatic Error Correction on Japanese Functional Expressions Using Character-based Neural Machine Translation
Jun Liu | Fei Cheng | Yiran Wang | Hiroyuki Shindo | Yuji Matsumoto
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation