Junfeng Yao


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EmoTrans: Emotional Transition-based Model for Emotion Recognition in Conversation
Zhongquan Jian | Ante Wang | Jinsong Su | Junfeng Yao | Meihong Wang | Qingqiang Wu
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

In an emotional conversation, emotions are causally transmitted among communication participants, constituting a fundamental conversational feature that can facilitate the comprehension of intricate changes in emotional states during the conversation and contribute to neutralizing emotional semantic bias in utterance caused by the absence of modality information. Therefore, emotional transition (ET) plays a crucial role in the task of Emotion Recognition in Conversation (ERC) that has not received sufficient attention in current research. In light of this, an Emotional Transition-based Emotion Recognizer (EmoTrans) is proposed in this paper. Specifically, we concatenate the most recent utterances with their corresponding speakers to construct the model input, known as samples, each with several placeholders to implicitly express the emotions of contextual utterances. Based on these placeholders, two components are developed to make the model sensitive to emotions and effectively capture the ET features in the sample. Furthermore, an ET-based Contrastive Learning (CL) is developed to compact the representation space, making the model achieve more robust sample representations. We conducted exhaustive experiments on four widely used datasets and obtained competitive experimental results, especially, new state-of-the-art results obtained on MELD and IEMOCAP, demonstrating the superiority of EmoTrans.


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Revisiting Non-Autoregressive Translation at Scale
Zhihao Wang | Longyue Wang | Jinsong Su | Junfeng Yao | Zhaopeng Tu
Findings of the Association for Computational Linguistics: ACL 2023

In real-world systems, scaling has been critical for improving the translation quality in autoregressive translation (AT), which however has not been well studied for non-autoregressive translation (NAT). In this work, we bridge the gap by systematically studying the impact of scaling on NAT behaviors. Extensive experiments on six WMT benchmarks over two advanced NAT models show that scaling can alleviate the commonly-cited weaknesses of NAT models, resulting in better translation performance. To reduce the side-effect of scaling on decoding speed, we empirically investigate the impact of NAT encoder and decoder on the translation performance. Experimental results on the large-scale WMT20 En-De show that the asymmetric architecture (e.g. bigger encoder and smaller decoder) can achieve comparable performance with the scaling model, while maintaining the superiority of decoding speed with standard NAT models. To this end, we establish a new benchmark by validating scaled NAT models on the scaled dataset, which can be regarded as a strong baseline for future works. We release code and system outputs at https://github.com/DeepLearnXMU/Scaling4NAT.


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Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings
Shaopeng Lai | Ante Wang | Fandong Meng | Jie Zhou | Yubin Ge | Jiali Zeng | Junfeng Yao | Degen Huang | Jinsong Su
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models, which inituitively possess complementary advantages. In this paper, we propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering (Yin et al. 2019, 2021). Specially, given an initial sentence-entity graph, we first introduce a graph-based classifier to predict pairwise orderings between linked sentences. Then, in an iterative manner, based on the graph updated by previously predicted high-confident pairwise orderings, another classifier is used to predict the remaining uncertain pairwise orderings. At last, we adapt a GRN-based sentence ordering model (Yin et al. 2019, 2021) on the basis of final graph. Experiments on five commonly-used datasets demonstrate the effectiveness and generality of our model. Particularly, when equipped with BERT (Devlin et al. 2019) and FHDecoder (Yin et al. 2020), our model achieves state-of-the-art performance. Our code is available at https://github.com/DeepLearnXMU/IRSEG.


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A Context-Aware Topic Model for Statistical Machine Translation
Jinsong Su | Deyi Xiong | Yang Liu | Xianpei Han | Hongyu Lin | Junfeng Yao | Min Zhang
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Graph-Based Collective Lexical Selection for Statistical Machine Translation
Jinsong Su | Deyi Xiong | Shujian Huang | Xianpei Han | Junfeng Yao
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Bilingual Correspondence Recursive Autoencoder for Statistical Machine Translation
Jinsong Su | Deyi Xiong | Biao Zhang | Yang Liu | Junfeng Yao | Min Zhang
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition
Biao Zhang | Jinsong Su | Deyi Xiong | Yaojie Lu | Hong Duan | Junfeng Yao
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing