The DocRED dataset is one of the most popular and widely used benchmarks for documentlevel relation extraction (RE).It adopts a recommend-revise annotation scheme so as to have a large-scale annotated dataset.However, we find that the annotation of DocRED is incomplete, i.e., false negative samples are prevalent.We analyze the causes and effects of the overwhelming false negative problem in the DocRED dataset.To address the shortcoming, we re-annotate 4,053 documents in the DocRED dataset by adding the missed relation triples back to the original DocRED.We name our revised DocRED dataset Re-DocRED.We conduct extensive experiments with state-ofthe-art neural models on both datasets, and the experimental results show that the models trained and evaluated on our Re-DocRED achieve performance improvements of around 13 F1 points.Moreover, we conduct a comprehensive analysis to identify the potential areas for further improvement.1

Lu Xu, Yew Ken Chia, Lidong Bing. 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). 2021.

It has been shown that named entity recognition (NER) could benefit from incorporating the long-distance structured information captured by dependency trees.We believe this is because both types of features -the contextual information captured by the linear sequences and the structured information captured by the dependency trees may complement each other.However, existing approaches largely focused on stacking the LSTM and graph neural networks such as graph convolutional networks (GCNs) for building improved NER models, where the exact interaction mechanism between the two different types of features is not very clear, and the performance gain does not appear to be significant.In this work, we propose a simple and robust solution to incorporate both types of features with our Synergized-LSTM (Syn-LSTM), which clearly captures how the two types of features interact.We conduct extensive experiments on several standard datasets across four languages.The results demonstrate that the proposed model achieves better performance than previous approaches while requiring fewer parameters.Our further analysis demonstrates that our model can capture longer dependencies compared with strong baselines.1

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from “Waiters are very friendly and the pasta is simply average” could be (‘Waiters’, positive, ‘friendly’). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.

Aspect sentiment classification, predicting the sentiment polarity of given aspects, has drawn extensive attention.Previous attention-based models emphasize using aspect semantics to help extract opinion features for classification.However, these works are either not able to capture opinion spans as a whole or capture variable-length opinion spans.In this paper, we present a neat and effective multiple CRFs based structured attention model that is capable of extracting aspect-specific opinion spans.The sentiment polarity of the target is then classified based on the extracted opinion features and contextual information.The experimental results on four datasets demonstrate the effectiveness of the proposed model, and our further analysis shows that our model can capture aspect-specific opinion spans. 1

Aspect Sentiment Triplet Extraction (ASTE)is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment.Existing research efforts mostly solve this problem using pipeline approaches, which break the triplet extraction process into several stages.Our observation is that the three elements within a triplet are highly related to each other, and this motivates us to build a joint model to extract such triplets using a sequence tagging approach.However, how to effectively design a tagging approach to extract the triplets that can capture the rich interactions among the elements is a challenging research question.In this work, we propose the first end-to-end model with a novel positionaware tagging scheme that is capable of jointly extracting the triplets.Our experimental results on several existing datasets show that jointly capturing elements in the triplet using our approach leads to improved performance over the existing approaches.We also conducted extensive experiments to investigate the model effectiveness and robustness 1 .