Jianping Shen

Also published as: JIanping Shen


2021

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RG PA at SemEval-2021 Task 1: A Contextual Attention-based Model with RoBERTa for Lexical Complexity Prediction
Gang Rao | Maochang Li | Xiaolong Hou | Lianxin Jiang | Yang Mo | Jianping Shen
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

In this paper we propose a contextual attention based model with two-stage fine-tune training using RoBERTa. First, we perform the first-stage fine-tune on corpus with RoBERTa, so that the model can learn some prior domain knowledge. Then we get the contextual embedding of context words based on the token-level embedding with the fine-tuned model. And we use Kfold cross-validation to get K models and ensemble them to get the final result. Finally, we attain the 2nd place in the final evaluation phase of sub-task 2 with pearson correlation of 0.8575.

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PALI at SemEval-2021 Task 2: Fine-Tune XLM-RoBERTa for Word in Context Disambiguation
Shuyi Xie | Jian Ma | Haiqin Yang | Lianxin Jiang | Yang Mo | Jianping Shen
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper presents the PALI team’s winning system for SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation. We fine-tune XLM-RoBERTa model to solve the task of word in context disambiguation, i.e., to determine whether the target word in the two contexts contains the same meaning or not. In implementation, we first specifically design an input tag to emphasize the target word in the contexts. Second, we construct a new vector on the fine-tuned embeddings from XLM-RoBERTa and feed it to a fully-connected network to output the probability of whether the target word in the context has the same meaning or not. The new vector is attained by concatenating the embedding of the [CLS] token and the embeddings of the target word in the contexts. In training, we explore several tricks, such as the Ranger optimizer, data augmentation, and adversarial training, to improve the model prediction. Consequently, we attain the first place in all four cross-lingual tasks.

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FPAI at SemEval-2021 Task 6: BERT-MRC for Propaganda Techniques Detection
Xiaolong Hou | Junsong Ren | Gang Rao | Lianxin Lian | Zhihao Ruan | Yang Mo | JIanping Shen
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

The objective of subtask 2 of SemEval-2021 Task 6 is to identify techniques used together with the span(s) of text covered by each technique. This paper describes the system and model we developed for the task. We first propose a pipeline system to identify spans, then to classify the technique in the input sequence. But it severely suffers from handling the overlapping in nested span. Then we propose to formulize the task as a question answering task by MRC framework which achieves a better result compared to the pipeline method. Moreover, data augmentation and loss design techniques are also explored to alleviate the problem of data sparse and imbalance. Finally, we attain the 3rd place in the final evaluation phase.

2020

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UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction
ShuYi Xie | Jian Ma | Haiqin Yang | Jiang Lianxin | Mo Yang | Jianping Shen
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Definition Extraction is the task to automatically extract terms and their definitions from text. In recent years, it attracts wide interest from NLP researchers. This paper describes the unixlong team’s system for the SemEval 2020 task6: DeftEval: Extracting term-definition pairs in free text. The goal of this task is to extract definition, word level BIO tags and relations. This task is challenging due to the free style of the text, especially the definitions of the terms range across several sentences and lack explicit verb phrases. We propose a joint model to train the tasks of definition extraction and the word level BIO tagging simultaneously. We design a creative format input of BERT to capture the location information between entity and its definition. Then we adjust the result of BERT with some rules. Finally, we apply TAG_ID, ROOT_ID, BIO tag to predict the relation and achieve macro-averaged F1 score 1.0 which rank first on the official test set in the relation extraction subtask.

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XSYSIGMA at SemEval-2020 Task 7: Method for Predicting Headlines’ Humor Based on Auxiliary Sentences with EI-BERT
Jian Ma | ShuYi Xie | Meizhi Jin | Jiang Lianxin | Mo Yang | Jianping Shen
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes xsysigma team’s system for SemEval 2020 Task 7: Assessing the Funniness of Edited News Headlines. The target of this task is to assess the funniness changes of news headlines after minor editing and is divided into two subtasks: Subtask 1 is a regression task to detect the humor intensity of the sentence after editing; and Subtask 2 is a classification task to predict funnier of the two edited versions of an original headline. In this paper, we only report our implement of Subtask 2. We first construct sentence pairs with different features for Enhancement Inference BERT(EI-BERT)’s input. We then conduct data augmentation strategy and Pseudo-Label method. After that, we apply feature enhancement interaction on the encoding of each sentence for classification with EI-BERT. Finally, we apply weighted fusion algorithm to the logits results which obtained by different pre-trained models. We achieve 64.5% accuracy in subtask2 and rank the first and the fifth in dev and test dataset 1 , respectively.

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FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection
Chenyang Guo | Xiaolong Hou | Junsong Ren | Lianxin Jiang | Yang Mo | Haiqin Yang | Jianping Shen
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes the model we apply in the SemEval-2020 Task 10. We formalize the task of emphasis selection as a simplified query-based machine reading comprehension (MRC) task, i.e. answering a fixed question of “Find candidates for emphasis”. We propose our subword puzzle encoding mechanism and subword fusion layer to align and fuse subwords. By introducing the semantic prior knowledge of the informative query and some other techniques, we attain the 7th place during the evaluation phase and the first place during train phase.

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SQL Generation via Machine Reading Comprehension
Zeyu Yan | Jianqiang Ma | Yang Zhang | Jianping Shen
Proceedings of the 28th International Conference on Computational Linguistics

Text-to-SQL systems offers natural language interfaces to databases, which can automatically generates SQL queries given natural language questions. On the WikiSQL benchmark, state-of- the-art text-to-SQL systems typically take a slot-filling approach by building several specialized models for each type of slot. Despite being effective, such modularized systems are complex and also fall short in jointly learning for different slots. To solve these problems, this paper proposes a novel approach that formulates the task as a question answering problem, where different slots are predicted by a unified machine reading comprehension (MRC) model. For this purpose, we use a BERT-based MRC model, which can also benefit from intermediate training on other MRC datasets. The proposed method can achieve competitive results on WikiSQL, suggesting it being a promising direction for text-to-SQL.

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FASTMATCH: Accelerating the Inference of BERT-based Text Matching
Shuai Pang | Jianqiang Ma | Zeyu Yan | Yang Zhang | Jianping Shen
Proceedings of the 28th International Conference on Computational Linguistics

Recently, pre-trained language models such as BERT have shown state-of-the-art accuracies in text matching. When being applied to IR (or QA), the BERT-based matching models need to online calculate the representations and interactions for all query-candidate pairs. The high inference cost has prohibited the deployments of BERT-based matching models in many practical applications. To address this issue, we propose a novel BERT-based text matching model, in which the representations and the interactions are decoupled. Then, the representations of the candidates can be calculated and stored offline, and directly retrieved during the online matching phase. To conduct the interactions and generate final matching scores, a lightweight attention network is designed. Experiments based on several large scale text matching datasets show that the proposed model, called FASTMATCH, can achieve up to 100X speed-up to BERT and RoBERTa at the online matching phase, while keeping more up to 98.7% of the performance.

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Task-Completion Dialogue Policy Learning via Monte Carlo Tree Search with Dueling Network
Sihan Wang | Kaijie Zhou | Kunfeng Lai | Jianping Shen
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We introduce a framework of Monte Carlo Tree Search with Double-q Dueling network (MCTS-DDU) for task-completion dialogue policy learning. Different from the previous deep model-based reinforcement learning methods, which uses background planning and may suffer from low-quality simulated experiences, MCTS-DDU performs decision-time planning based on dialogue state search trees built by Monte Carlo simulations and is robust to the simulation errors. Such idea arises naturally in human behaviors, e.g. predicting others’ responses and then deciding our own actions. In the simulated movie-ticket booking task, our method outperforms the background planning approaches significantly. We demonstrate the effectiveness of MCTS and the dueling network in detailed ablation studies, and also compare the performance upper bounds of these two planning methods.

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Mention Extraction and Linking for SQL Query Generation
Jianqiang Ma | Zeyu Yan | Shuai Pang | Yang Zhang | Jianping Shen
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

On the WikiSQL benchmark, state-of-the-art text-to-SQL systems typically take a slot- filling approach by building several dedicated models for each type of slots. Such modularized systems are not only complex but also of limited capacity for capturing inter-dependencies among SQL clauses. To solve these problems, this paper proposes a novel extraction-linking approach, where a unified extractor recognizes all types of slot mentions appearing in the question sentence before a linker maps the recognized columns to the table schema to generate executable SQL queries. Trained with automatically generated annotations, the proposed method achieves the first place on the WikiSQL benchmark.

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Make Templates Smarter: A Template Based Data2Text System Powered by Text Stitch Model
Bingfeng Luo | Zuo Bai | Kunfeng Lai | Jianping Shen
Findings of the Association for Computational Linguistics: EMNLP 2020

Neural network (NN) based data2text models achieve state-of-the-art (SOTA) performance in most metrics, but they sometimes drop or modify the information in the input, and it is hard to control the generation contents. Moreover, it requires paired training data that are usually expensive to collect. Template-based methods have good fidelity and controllability but require heavy human involvement. We propose a novel template-based data2text system powered by a text stitch model. It ensures fidelity and controllability by using templates to produce the main contents. In addition, it reduces human involvement in template design by using a text stitch model to automatically stitch adjacent template units, which is a step that usually requires careful template design and limits template reusability. The text stitch model can be trained in self-supervised fashion, which only requires free texts. The experiments on a benchmark dataset show that our system outperforms SOTA NN-based systems in fidelity and surpasses template-based systems in diversity and human involvement.

2010

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Exploiting Rich Features for Detecting Hedges and their Scope
Xinxin Li | Jianping Shen | Xiang Gao | Xuan Wang
Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task

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Chinese Word Segmentation based on Mixing Multiple Preprocessor and CRF
Jianping Shen | Xuan Wang | Hainan Zhao | Wenxiao Zhang
CIPS-SIGHAN Joint Conference on Chinese Language Processing