Junyu Lu

Also published as: JunYu Lu


2023

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ZBL2W at SemEval-2023 Task 9: A Multilingual Fine-tuning Model with Data Augmentation for Tweet Intimacy Analysis
Hao Zhang | Youlin Wu | Junyu Lu | Zewen Bai | Jiangming Wu | Hongfei Lin | Shaowu Zhang
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

This paper describes our system used in the SemEval-2023 Task 9 Multilingual Tweet Intimacy Analysis. There are two key challenges in this task: the complexity of multilingual and zero-shot cross-lingual learning, and the difficulty of semantic mining of tweet intimacy. To solve the above problems, our system extracts contextual representations from the pretrained language models, XLM-T, and employs various optimization methods, including adversarial training, data augmentation, ordinal regression loss and special training strategy. Our system ranked 14th out of 54 participating teams on the leaderboard and ranked 10th on predicting languages not in the training data. Our code is available on Github.

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Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks
Junyu Lu | Bo Xu | Xiaokun Zhang | Changrong Min | Liang Yang | Hongfei Lin
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The widespread dissemination of toxic online posts is increasingly damaging to society. However, research on detecting toxic language in Chinese has lagged significantly due to limited datasets. Existing datasets suffer from a lack of fine-grained annotations, such as the toxic type and expressions with indirect toxicity. These fine-grained annotations are crucial factors for accurately detecting the toxicity of posts involved with lexical knowledge, which has been a challenge for researchers. To tackle this problem, we facilitate the fine-grained detection of Chinese toxic language by building a new dataset with benchmark results. First, we devised Monitor Toxic Frame, a hierarchical taxonomy to analyze the toxic type and expressions. Then, we built a fine-grained dataset ToxiCN, including both direct and indirect toxic samples. ToxiCN is based on an insulting vocabulary containing implicit profanity. We further propose a benchmark model, Toxic Knowledge Enhancement (TKE), by incorporating lexical features to detect toxic language. We demonstrate the usability of ToxiCN and the effectiveness of TKE based on a systematic quantitative and qualitative analysis.

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UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective
Yang Ping | JunYu Lu | Ruyi Gan | Junjie Wang | Yuxiang Zhang | Pingjian Zhang | Jiaxing Zhang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, relation extraction, event extraction and sentiment analysis. Our approach converts the text-based IE tasks as the token-pair problem, which uniformly disassembles all extraction targets into joint span detection, classification and association problems with a unified extractive framework, namely UniEX. UniEX can synchronously encode schema-based prompt and textual information, and collaboratively learn the generalized knowledge from pre-defined information using the auto-encoder language models. We develop a traffine attention mechanism to integrate heterogeneous factors including tasks, labels and inside tokens, and obtain the extraction target via a scoring matrix. Experiment results show that UniEX can outperform generative universal IE models in terms of performance and inference-speed on 14 benchmarks IE datasets with the supervised setting. The state-of-the-art performance in low-resource scenarios also verifies the transferability and effectiveness of UniEX.

2022

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GUTS at SemEval-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection
Junyu Lu | Hao Zhang | Tongyue Zhang | Hongbo Wang | Haohao Zhu | Bo Xu | Hongfei Lin
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Patronizing and Condescending Language (PCL) towards vulnerable communities in general media has been shown to have potentially harmful effects. Due to its subtlety and the good intentions behind its use, the audience is not aware of the language’s toxicity. In this paper, we present our method for the SemEval-2022 Task4 titled “Patronizing and Condescending Language Detection”. In Subtask A, a binary classification task, we introduce adversarial training based on Fast Gradient Method (FGM) and employ pre-trained model in a unified architecture. For Subtask B, framed as a multi-label classification problem, we utilize various improved multi-label cross-entropy loss functions and analyze the performance of our method. In the final evaluation, our system achieved official rankings of 17/79 and 16/49 on Subtask A and Subtask B, respectively. In addition, we explore the relationship between PCL and emotional polarity and intensity it contains.

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Flat Multi-modal Interaction Transformer for Named Entity Recognition
Junyu Lu | Dixiang Zhang | Jiaxing Zhang | Pingjian Zhang
Proceedings of the 29th International Conference on Computational Linguistics

Multi-modal named entity recognition (MNER) aims at identifying entity spans and recognizing their categories in social media posts with the aid of images. However, in dominant MNER approaches, the interaction of different modalities is usually carried out through the alternation of self-attention and cross-attention or over-reliance on the gating machine, which results in imprecise and biased correspondence between fine-grained semantic units of text and image. To address this issue, we propose a Flat Multi-modal Interaction Transformer (FMIT) for MNER. Specifically, we first utilize noun phrases in sentences and general domain words to obtain visual cues. Then, we transform the fine-grained semantic representation of the vision and text into a unified lattice structure and design a novel relative position encoding to match different modalities in Transformer. Meanwhile, we propose to leverage entity boundary detection as an auxiliary task to alleviate visual bias. Experiments show that our methods achieve the new state-of-the-art performance on two benchmark datasets.

2020

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SiNER: A Large Dataset for Sindhi Named Entity Recognition
Wazir Ali | Junyu Lu | Zenglin Xu
Proceedings of the Twelfth Language Resources and Evaluation Conference

We introduce the SiNER: a named entity recognition (NER) dataset for low-resourced Sindhi language with quality baselines. It contains 1,338 news articles and more than 1.35 million tokens collected from Kawish and Awami Awaz Sindhi newspapers using the begin-inside-outside (BIO) tagging scheme. The proposed dataset is likely to be a significant resource for statistical Sindhi language processing. The ultimate goal of developing SiNER is to present a gold-standard dataset for Sindhi NER along with quality baselines. We implement several baseline approaches of conditional random field (CRF) and recent popular state-of-the-art bi-directional long-short term memory (Bi-LSTM) models. The promising F1-score of 89.16 outputted by the Bi-LSTM-CRF model with character-level representations demonstrates the quality of our proposed SiNER dataset.

2019

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Constructing Interpretive Spatio-Temporal Features for Multi-Turn Responses Selection
Junyu Lu | Chenbin Zhang | Zeying Xie | Guang Ling | Tom Chao Zhou | Zenglin Xu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Response selection plays an important role in fully automated dialogue systems. Given the dialogue context, the goal of response selection is to identify the best-matched next utterance (i.e., response) from multiple candidates. Despite the efforts of many previous useful models, this task remains challenging due to the huge semantic gap and also the large size of candidate set. To address these issues, we propose a Spatio-Temporal Matching network (STM) for response selection. In detail, soft alignment is first used to obtain the local relevance between the context and the response. And then, we construct spatio-temporal features by aggregating attention images in time dimension and make use of 3D convolution and pooling operations to extract matching information. Evaluation on two large-scale multi-turn response selection tasks has demonstrated that our proposed model significantly outperforms the state-of-the-art model. Particularly, visualization analysis shows that the spatio-temporal features enables matching information in segment pairs and time sequences, and have good interpretability for multi-turn text matching.