“Zero-shot relation extraction is an important method for dealing with the newly emerging relations in the real world which lacks labeled data. However, the mainstream two-tower zero-shot methods usually rely on large-scale and in-domain labeled data of predefined relations. In this work, we view zero-shot relation extraction as a semantic matching task optimized by prompt-tuning, which still maintains superior generalization performance when the labeled data of predefined relations are extremely scarce. To maximize the efficiency of data exploitation, instead of directly fine-tuning, we introduce a prompt-tuning technique to elicit the existing relational knowledge in pre-trained language model (PLMs). In addition, very few relation descriptions are exposed to the model during training, which we argue is the performance bottleneck of two-tower methods. To break through the bottleneck, we model the semantic interaction between relational instances and their descriptions directly during encoding. Experiment results on two academic datasets show that (1) our method outperforms the previous state-of-the-art method by a large margin with different samples of predefined relations; (2) this advantage will be further amplified in the low-resource scenario.”

“Supervised learning is a classic paradigm of relation extraction (RE). However, a well-performing model can still confidently make arbitrarily wrong predictions when exposed to samples of unseen relations. In this work, we propose a relation extraction method with rejection option to improve robustness to unseen relations. To enable the classifier to reject unseen relations, we introduce contrastive learning techniques and carefully design a set of class-preserving transformations to improve the discriminability between known and unseen relations. Based on the learned representation, inputs of unseen relations are assigned a low confidence score and rejected. Off-the-shelf open relation extraction (OpenRE) methods can be adopted to discover the potential relations in these rejected inputs. In addition, we find that the rejection can be further improved via readily available distantly supervised data. Experiments on two public datasets prove the effectiveness of our method capturing discriminative representations for unseen relation rejection.”

“Explanations can increase the transparency of neural networks and make them more trustworthy. However, can we really trust explanations generated by the existing explanation methods? If the explanation methods are not stable enough, the credibility of the explanation will be greatly reduced. Previous studies seldom considered such an important issue. To this end, this paper proposes a new evaluation frame to evaluate the stability of current typical feature attribution explanation methods via textual adversarial attack. Our frame could generate adversarial examples with similar textual semantics. Such adversarial examples will make the original models have the same outputs, but make most current explanation methods deduce completely different explanations. Under this frame, we test five classical explanation methods and show their performance on several stability-related metrics. Experimental results show our evaluation is effective and could reveal the stability performance of existing explanation methods.”

In this paper we focus on machine reading comprehension in social media. In this domain onenormally posts a message on the assumption that the readers have specific background knowledge. Therefore those messages are usually short and lacking in background information whichis different from the text in the other domain. Thus it is difficult for a machine to understandthe messages comprehensively. Fortunately a key nature of social media is clustering. A group of people tend to express their opinion or report news around one topic. Having realized this we propose a novel method that utilizes the topic knowledge implied by the clustered messages to aid in the comprehension of those short messages. The experiments on TweetQA datasets demonstrate the effectiveness of our method.

The irrelevant information in documents poses a great challenge for machine reading compre-hension (MRC). To deal with such a challenge current MRC models generally fall into twoseparate parts: evidence extraction and answer prediction where the former extracts the key evi-dence corresponding to the question and the latter predicts the answer based on those sentences.However such pipeline paradigms tend to accumulate errors i.e. extracting the incorrect evi-dence results in predicting the wrong answer. In order to address this problem we propose aMulti-Strategy Knowledge Distillation based Teacher-Student framework (MSKDTS) for ma-chine reading comprehension. In our approach we first take evidence and document respec-tively as the input reference information to build a teacher model and a student model. Then the multi-strategy knowledge distillation method transfers the knowledge from the teacher model to the student model at both feature and prediction level through knowledge distillation approach.Therefore in the testing phase the enhanced student model can predict answer similar to the teacher model without being aware of which sentence is the corresponding evidence in the docu-ment. Experimental results on the ReCO dataset demonstrate the effectiveness of our approachand further ablation studies prove the effectiveness of both knowledge distillation strategies.

In the paper we present a ‘pre-training’+‘post-training’+‘fine-tuning’ three-stage paradigm which is a supplementary framework for the standard ‘pre-training’+‘fine-tuning’ languagemodel approach. Furthermore based on three-stage paradigm we present a language modelnamed PPBERT. Compared with original BERT architecture that is based on the standard two-stage paradigm we do not fine-tune pre-trained model directly but rather post-train it on the domain or task related dataset first which helps to better incorporate task-awareness knowl-edge and domain-awareness knowledge within pre-trained model also from the training datasetreduce bias. Extensive experimental results indicate that proposed model improves the perfor-mance of the baselines on 24 NLP tasks which includes eight GLUE benchmarks eight Su-perGLUE benchmarks six extractive question answering benchmarks. More remarkably our proposed model is a more flexible and pluggable model where post-training approach is able to be plugged into other PLMs that are based on BERT. Extensive ablations further validate the effectiveness and its state-of-the-art (SOTA) performance. The open source code pre-trained models and post-trained models are available publicly.