A scientific claim typically begins with the formulation of a research question or hypothesis, which is a tentative statement or proposition about a phenomenon or relationship between variables.Within the realm of scientific claim verification, considerable research efforts have been dedicated to attention architectures and leveraging the text comprehension capabilities of Pre-trained Language Models (PLMs), yielding promising performances.However, these models overlook the causal structure information inherent in scientific claims, thereby failing to establish a comprehensive chain of causal inference.This paper delves into the exploration to highlight the crucial role of qualitative causal structure in characterizing and verifying scientific claims based on evidence.We organize the qualitative causal structure into a heterogeneous graph and propose a novel attentionbased graph neural network model to facilitate causal reasoning across relevant causallypotent factors.Our experiments demonstrate that by solely utilizing the qualitative causal structure, the proposed model achieves comparable performance to PLM-based models.Furthermore, by incorporating semantic features, our model outperforms state-of-the-art approaches comprehensively. 1

In this paper, we study the problem of identifying the principals and accessories from the fact description with multiple defendants in a criminal case.We treat the fact descriptions as narrative texts and the defendants as roles over the narrative story.We propose to model the defendants with behavioral semantic information and statistical characteristics, then learning the importances of defendants within a learning-to-rank framework.Experimental results on a real-world dataset demonstrate the behavior analysis can effectively model the defendants' impacts in a complex case.

Conventional approaches for formality style transfer borrow models from neural machine translation, which typically requires massive parallel data for training. However, the dataset for formality style transfer is considerably smaller than translation corpora. Moreover, we observe that informal and formal sentences closely resemble each other, which is different from the translation task where two languages have different vocabularies and grammars. In this paper, we present a new approach, Sequence-to-Sequence with Shared Latent Space (S2S-SLS), for formality style transfer, where we propose two auxiliary losses and adopt joint training of bi-directional transfer and auto-encoding. Experimental results show that S2S-SLS (with either RNN or Transformer architectures) consistently outperforms baselines in various settings, especially when we have limited data.

Yunli Wang, Yu Wu, Lili Mou, Zhoujun Li, Wenhan Chao. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

With the growth in popularity and size of social media, there is an urgent need for systems that can recognize the coreference relation between two event mentions in texts from social media. In existing event coreference resolution research, a rich set of linguistic features derived from pre-existing NLP tools and various knowledge bases is often required. This kind of methods restricts domain scalability and leads to the propagation of errors. In this paper, we present a novel selective expression approach based on event trigger to explore the coreferential relationship in high-volume Twitter texts. Firstly, we exploit a bidirectional Long Short Term Memory (Bi-LSTM) to extract the sentence level and mention level features. Then, to selectively express the essential parts of generated features, we apply a gate on sentence level features. Next, to integrate the time information of event mention pairs, we design an auxiliary feature based on triggers and time attributes of the two event mentions. Finally, all these features are concatenated and fed into a classifier to predict the binary coreference relationship between the event mention pair. To evaluate our method, we publish a new dataset EventCoreOnTweet (ECT)1that annotates the coreferential relationship between event mentions and event trigger of each event mention. The experimental results demonstrate that our approach achieves significant performance in the ECT dataset.

As DGA (Domain Generation Algorithm) detection technologies and systems become more and more complex, more types of AGD (Algorithmically Generated Domain) appear: Dictionary-based AGD, Hash-based AGD, etc. This paper applies deep learning to the field of network security, proposes a lightweight AGD detection approach, Themis, which can classify domain names into legitimate domain names or AGDs through domain name strings. Themis combines WordNet and GRU to capture the different characteristics of legitimate domain name and AGD for classification. Compared with the prior art, Themis has two differences: 1) Themis is the first approach to detect mixed AGD (Arithmetic-based and Dictionary-based); 2) Themis performs well in detecting unknowns AGD.

Charge prediction which aims to determine appropriate charges for criminal cases based on textual fact descriptions, is an important technology in the field of AI&Law. Previous works focus on improving prediction accuracy, ignoring the interpretability, which limits the methods’ applicability. In this work, we propose a deep neural framework to extract short but charge-decisive text snippets – rationales – from input fact description, as the interpretation of charge prediction. To solve the scarcity problem ofrationale annotatedcorpus, rationalesare extractedinareinforcement stylewiththe only supervision in the form of charge labels. We further propose a dynamic rationale attention mechanism to better utilize the information in extracted rationales and predict the charges. Experimental results show that besides providing charge prediction interpretation, our approach can also capture subtle details to help charge prediction.

Hai Ye, Xin Jiang, Zhunchen Luo, Wenhan Chao. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Connections between relations in relation extraction, which we call class ties, are common.In distantly supervised scenario, one entity tuple may have multiple relation facts.Exploiting class ties between relations of one entity tuple will be promising for distantly supervised relation extraction.However, previous models are not effective or ignore to model this property.In this work, to effectively leverage class ties, we propose to make joint relation extraction with a unified model that integrates convolutional neural network (CNN) with a general pairwise ranking framework, in which three novel ranking loss functions are introduced.Additionally, an effective method is presented to relieve the severe class imbalance problem from NR (not relation) for model training.Experiments on a widely used dataset show that leveraging class ties will enhance extraction and demonstrate the effectiveness of our model to learn class ties.Our model outperforms the baselines significantly, achieving stateof-the-art performance.

Resampling method is a popular and effective technique to imbalanced learning. However, most resampling methods ignore data density information and may lead to overfitting. A novel adaptive over-sampling technique based on data density (ASMOBD) is proposed in this paper. Compared with existing resampling algorithms, ASMOBD can adaptively synthesize different number of new samples around each minority sample according to its level of learning difficulty. Therefore, this method makes the decision region more specific and can eliminate noise. What's more, to avoid over generalization, two smoothing methods are proposed. Cost- Sensitive learning is also an effective technique to imbalanced learning. In this paper, ASMOBD and Cost-Sensitive SVM are combined. Experiments show that our methods perform better than various state-of-art approaches on 9 UCI datasets by using metrics of G-mean and area under the receiver operation curve (AUC).

This paper is concerned with the problem of question recommendation in the setting of Community Question Answering (CQA). Given a question as query, our goal is to rank all of the retrieved questions according to their likelihood of being good recommendations for the query. In this paper, we propose a notion of public interest, and show how public interest can boost the performance of question recommendation. In particular, to model public interest in question recommendation, we build a language model to combine relevance score to the query and popularity score regarding question popularity. Experimental results on Yahoo!Answers dataset demonstrate the performance of question recommendation can be greatly improved with considering the public interest.

In statistical machine translation, the number of sentence pairs in the bilingual corpus is very important to the quality of translation. However, when the quantity reaches some extent, enlarging corpus has less effect on the translation, whereas increasing greatly the time and space complexity to building translation systems, which hinders the development of statistical machine translation. In this paper, we propose several ranking approaches to measure the quantity of information of each sentence pair, and apply them into a graph-based bilingual corpus selection framework to form an improved corpus selection approach, which now considers the difference of the initial quantities of information between the sentence pairs. Our experiments in a Chinese-English translation task show that, selecting only 50% of the whole corpus via the graph-based selection approach as training set, we can obtain the near translation result with the one using the whole corpus, and we obtain better results than the baselines after using the IDF-related ranking approach.