Xiao Ding


2022

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A Graph Enhanced BERT Model for Event Prediction
Li Du | Xiao Ding | Yue Zhang | Ting Liu | Bing Qin
Findings of the Association for Computational Linguistics: ACL 2022

Predicting the subsequent event for an existing event context is an important but challenging task, as it requires understanding the underlying relationship between events. Previous methods propose to retrieve relational features from event graph to enhance the modeling of event correlation. However, the sparsity of event graph may restrict the acquisition of relevant graph information, and hence influence the model performance. To address this issue, we consider automatically building of event graph using a BERT model. To this end, we incorporate an additional structured variable into BERT to learn to predict the event connections in the training process.Hence, in the test process, the connection relationship for unseen events can be predicted by the structured variable.Results on two event prediction tasks: script event prediction and story ending prediction, show that our approach can outperform state-of-the-art baseline methods.

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CogBERT: Cognition-Guided Pre-trained Language Models
Xiao Ding | Bowen Chen | Li Du | Bing Qin | Ting Liu
Proceedings of the 29th International Conference on Computational Linguistics

We study the problem of integrating cognitive language processing signals (e.g., eye-tracking or EEG data) into pre-trained language models like BERT. Existing methods typically fine-tune pre-trained models on cognitive data, ignoring the semantic gap between the texts and cognitive signals. To fill the gap, we propose CogBERT, a framework that can induce fine-grained cognitive features from cognitive data and incorporate cognitive features into BERT by adaptively adjusting the weight of cognitive features for different NLP tasks. Extensive experiments show that: (1) Cognition-guided pre-trained models can consistently perform better than basic pre-trained models on ten NLP tasks. (2) Different cognitive features contribute differently to different NLP tasks. Based on this observation, we give a fine-grained explanation of why cognitive data is helpful for NLP. (3) Different transformer layers of pre-trained models should encode different cognitive features, with word-level cognitive features at the bottom and semantic-level cognitive features at the top. (4) Attention visualization demonstrates that CogBERT aligns with human gaze patterns and improves its natural language comprehension ability.

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e-CARE: a New Dataset for Exploring Explainable Causal Reasoning
Li Du | Xiao Ding | Kai Xiong | Ting Liu | Bing Qin
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can provide deep understanding of the causal fact to facilitate the causal reasoning process. However, such explanation information still remains absent in existing causal reasoning resources. In this paper, we fill this gap by presenting a human-annotated explainable CAusal REasoning dataset (e-CARE), which contains over 20K causal reasoning questions, together with natural language formed explanations of the causal questions. Experimental results show that generating valid explanations for causal facts still remains especially challenging for the state-of-the-art models, and the explanation information can be helpful for promoting the accuracy and stability of causal reasoning models.

2021

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Neural Natural Logic Inference for Interpretable Question Answering
Jihao Shi | Xiao Ding | Li Du | Ting Liu | Bing Qin
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Many open-domain question answering problems can be cast as a textual entailment task, where a question and candidate answers are concatenated to form hypotheses. A QA system then determines if the supporting knowledge bases, regarded as potential premises, entail the hypotheses. In this paper, we investigate a neural-symbolic QA approach that integrates natural logic reasoning within deep learning architectures, towards developing effective and yet explainable question answering models. The proposed model gradually bridges a hypothesis and candidate premises following natural logic inference steps to build proof paths. Entailment scores between the acquired intermediate hypotheses and candidate premises are measured to determine if a premise entails the hypothesis. As the natural logic reasoning process forms a tree-like, hierarchical structure, we embed hypotheses and premises in a Hyperbolic space rather than Euclidean space to acquire more precise representations. Empirically, our method outperforms prior work on answering multiple-choice science questions, achieving the best results on two publicly available datasets. The natural logic inference process inherently provides evidence to help explain the prediction process.

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ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning
Li Du | Xiao Ding | Kai Xiong | Ting Liu | Bing Qin
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)

Prior work infers the causation between events mainly based on the knowledge induced from the annotated causal event pairs. However, additional evidence information intermediate to the cause and effect remains unexploited. By incorporating such information, the logical law behind the causality can be unveiled, and the interpretability and stability of the causal reasoning system can be improved. To facilitate this, we present an Event graph knowledge enhanced explainable CAusal Reasoning framework (ExCAR). ExCAR first acquires additional evidence information from a large-scale causal event graph as logical rules for causal reasoning. To learn the conditional probabilistic of logical rules, we propose the Conditional Markov Neural Logic Network (CMNLN) that combines the representation learning and structure learning of logical rules in an end-to-end differentiable manner. Experimental results demonstrate that ExCAR outperforms previous state-of-the-art methods. Adversarial evaluation shows the improved stability of ExCAR over baseline systems. Human evaluation shows that ExCAR can achieve a promising explainable performance.

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Learning Event Graph Knowledge for Abductive Reasoning
Li Du | Xiao Ding | Ting Liu | Bing Qin
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)

Abductive reasoning aims at inferring the most plausible explanation for observed events, which would play critical roles in various NLP applications, such as reading comprehension and question answering. To facilitate this task, a narrative text based abductive reasoning task 𝛼NLI is proposed, together with explorations about building reasoning framework using pretrained language models. However, abundant event commonsense knowledge is not well exploited for this task. To fill this gap, we propose a variational autoencoder based model ege-RoBERTa, which employs a latent variable to capture the necessary commonsense knowledge from event graph for guiding the abductive reasoning task. Experimental results show that through learning the external event graph knowledge, our approach outperforms the baseline methods on the 𝛼NLI task.

2020

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HIT-SCIR at SemEval-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection
Xiao Ding | Dingkui Hao | Yuewei Zhang | Kuo Liao | Zhongyang Li | Bing Qin | Ting Liu
Proceedings of the Fourteenth Workshop on Semantic Evaluation

We describe our system for Task 5 of SemEval 2020: Modelling Causal Reasoning in Language: Detecting Counterfactuals. Despite deep learning has achieved significant success in many fields, it still hardly drives today’s AI to strong AI, as it lacks of causation, which is a fundamental concept in human thinking and reasoning. In this task, we dedicate to detecting causation, especially counterfactuals from texts. We explore multiple pre-trained models to learn basic features and then fine-tune models with counterfactual data and pseudo-labeling data. Our team HIT-SCIR wins the first place (1st) in Sub-task 1 — Detecting Counterfactual Statements and is ranked 4th in Sub-task 2 — Detecting Antecedent and Consequence. In this paper we provide a detailed description of the approach, as well as the results obtained in this task.

2019

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Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder
Li Du | Xiao Ding | Ting Liu | Zhongyang Li
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Understanding event and event-centered commonsense reasoning are crucial for natural language processing (NLP). Given an observed event, it is trivial for human to infer its intents and effects, while this type of If-Then reasoning still remains challenging for NLP systems. To facilitate this, a If-Then commonsense reasoning dataset Atomic is proposed, together with an RNN-based Seq2Seq model to conduct such reasoning. However, two fundamental problems still need to be addressed: first, the intents of an event may be multiple, while the generations of RNN-based Seq2Seq models are always semantically close; second, external knowledge of the event background may be necessary for understanding events and conducting the If-Then reasoning. To address these issues, we propose a novel context-aware variational autoencoder effectively learning event background information to guide the If-Then reasoning. Experimental results show that our approach improves the accuracy and diversity of inferences compared with state-of-the-art baseline methods.

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Event Representation Learning Enhanced with External Commonsense Knowledge
Xiao Ding | Kuo Liao | Ting Liu | Zhongyang Li | Junwen Duan
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction. On the other hand, events extracted from raw texts lacks of commonsense knowledge, such as the intents and emotions of the event participants, which are useful for distinguishing event pairs when there are only subtle differences in their surface realizations. To address this issue, this paper proposes to leverage external commonsense knowledge about the intent and sentiment of the event. Experiments on three event-related tasks, i.e., event similarity, script event prediction and stock market prediction, show that our model obtains much better event embeddings for the tasks, achieving 78% improvements on hard similarity task, yielding more precise inferences on subsequent events under given contexts, and better accuracies in predicting the volatilities of the stock market.

2018

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Learning Sentence Representations over Tree Structures for Target-Dependent Classification
Junwen Duan | Xiao Ding | Ting Liu
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Target-dependent classification tasks, such as aspect-level sentiment analysis, perform fine-grained classifications towards specific targets. Semantic compositions over tree structures are promising for such tasks, as they can potentially capture long-distance interactions between targets and their contexts. However, previous work that operates on tree structures resorts to syntactic parsers or Treebank annotations, which are either subject to noise in informal texts or highly expensive to obtain. To address above issues, we propose a reinforcement learning based approach, which automatically induces target-specific sentence representations over tree structures. The underlying model is a RNN encoder-decoder that explores possible binary tree structures and a reward mechanism that encourages structures that improve performances on downstream tasks. We evaluate our approach on two benchmark tasks: firm-specific cumulative abnormal return prediction (based on formal news texts) and aspect-level sentiment analysis (based on informal social media texts). Experimental results show that our model gives superior performances compared to previous work that operates on parsed trees. Moreover, our approach gives some intuitions on how target-specific sentence representations can be achieved from its word constituents.

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Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training
Zhongyang Li | Xiao Ding | Ting Liu
Proceedings of the 27th International Conference on Computational Linguistics

Story generation is a challenging problem in artificial intelligence (AI) and has received a lot of interests in the natural language processing (NLP) community. Most previous work tried to solve this problem using Sequence to Sequence (Seq2Seq) model trained with Maximum Likelihood Estimation (MLE). However, the pure MLE training objective much limits the power of Seq2Seq model in generating high-quality storys. In this paper, we propose using adversarial training augmented Seq2Seq model to generate reasonable and diversified story endings given a story context. Our model includes a generator that defines the policy of generating a story ending, and a discriminator that labels story endings as human-generated or machine-generated. Carefully designed human and automatic evaluation metrics demonstrate that our adversarial training augmented Seq2Seq model can generate more reasonable and diversified story endings compared to purely MLE-trained Seq2Seq model. Moreover, our model achieves better performance on the task of Story Cloze Test with an accuracy of 62.6% compared with state-of-the-art baseline methods.

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Learning Target-Specific Representations of Financial News Documents For Cumulative Abnormal Return Prediction
Junwen Duan | Yue Zhang | Xiao Ding | Ching-Yun Chang | Ting Liu
Proceedings of the 27th International Conference on Computational Linguistics

Texts from the Internet serve as important data sources for financial market modeling. Early statistical approaches rely on manually defined features to capture lexical, sentiment and event information, which suffers from feature sparsity. Recent work has considered learning dense representations for news titles and abstracts. Compared to news titles, full documents can contain more potentially helpful information, but also noise compared to events and sentences, which has been less investigated in previous work. To fill this gap, we propose a novel target-specific abstract-guided news document representation model. The model uses a target-sensitive representation of the news abstract to weigh sentences in the news content, so as to select and combine the most informative sentences for market modeling. Results show that document representations can give better performance for estimating cumulative abnormal returns of companies when compared to titles and abstracts. Our model is especially effective when it used to combine information from multiple document sources compared to the sentence-level baselines.

2017

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Benben: A Chinese Intelligent Conversational Robot
Wei-Nan Zhang | Ting Liu | Bing Qin | Yu Zhang | Wanxiang Che | Yanyan Zhao | Xiao Ding
Proceedings of ACL 2017, System Demonstrations

2016

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Knowledge-Driven Event Embedding for Stock Prediction
Xiao Ding | Yue Zhang | Ting Liu | Junwen Duan
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Representing structured events as vectors in continuous space offers a new way for defining dense features for natural language processing (NLP) applications. Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as event-driven stock prediction. On the other hand, events extracted from raw texts do not contain background knowledge on entities and relations that they are mentioned. To address this issue, this paper proposes to leverage extra information from knowledge graph, which provides ground truth such as attributes and properties of entities and encodes valuable relations between entities. Specifically, we propose a joint model to combine knowledge graph information into the objective function of an event embedding learning model. Experiments on event similarity and stock market prediction show that our model is more capable of obtaining better event embeddings and making more accurate prediction on stock market volatilities.

2014

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Using Structured Events to Predict Stock Price Movement: An Empirical Investigation
Xiao Ding | Yue Zhang | Ting Liu | Junwen Duan
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Improving Web Search Ranking by Incorporating Structured Annotation of Queries
Xiao Ding | Zhicheng Dou | Bing Qin | Ting Liu | Ji-Rong Wen
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Building Chinese Event Type Paradigm Based on Trigger Clustering
Xiao Ding | Bing Qin | Ting Liu
Proceedings of the Sixth International Joint Conference on Natural Language Processing