Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

In fighting the COVID-19 pandemic, the main challenges include the lack of prior research and the urgency to find effective solutions. It is essential to accurately and rapidly summarize the relevant research work and explore potential solutions for diagnosis, treatment and prevention of COVID-19. It is a daunting task to summarize the numerous existing research works and to assess their effectiveness. This paper explores the discovery of new COVID-19 research approaches based on dynamic link prediction, which analyze the dynamic topological network of keywords to predict possible connections of research concepts. A dynamic link prediction method based on multi-granularity feature fusion is proposed. Firstly, a multi-granularity temporal feature fusion method is adopted to extract the temporal evolution of different order subgraphs. Secondly, a hierarchical feature weighting method is proposed to emphasize actively evolving nodes. Thirdly, a semantic repetition sampling mechanism is designed to avoid the negative effect of semantically equivalent medical entities on the real structure of the graph, and to capture the real topological structure features. Experiments are performed on the COVID-19 Open Research Dataset to assess the performance of the model. The results show that the proposed model performs significantly better than existing state-of-the-art models, thereby confirming the effectiveness of the proposed method for the discovery of new COVID-19 research approaches.

Dynamic Link Prediction for Discovery of New Impactful COVID-19 Research Approaches

In recent years, named entity recognition (NER) tasks in the Indonesian language have undergone extensive development. There are only a few corpora for Indonesian NER; hence, recent Indonesian NER studies have used diverse datasets. Although an open dataset is available, it includes only approximately 2,000 sentences and contains inconsistent annotations, thereby preventing accurate training of NER models without reliance on pre-trained models. Therefore, we re-annotated the dataset and compared the two annotations’ performance using the Bidirectional Long Short-Term Memory and Conditional Random Field (BiLSTM-CRF) approach. Fixing the annotation yielded a more consistent result for the organization tag and improved the prediction score by a large margin. Moreover, to take full advantage of pre-trained models, we compared different feature embeddings to determine their impact on the NER task for the Indonesian language.

Towards a Standardized Dataset on Indonesian Named Entity Recognition.

This paper mainly studies the implementation of diverse conversation generation system based on end-to-end neural networks and sentence classification on sentence function. In existing work, the output of the conversational system is mostly in the form of one type of sentence, for instance, declarative sentence. The type of sentence function is not well distributed. There is still insufficient diversity in the output of conversational system, which could be unattractive to users. A good conversational system could well interact with users by generating diverse output, including asking and responding, driving conversations to go further. Generating different type of output sentence is necessary to conversational systems, which is also challenging. Therefore, this paper introduces the idea of diverse conversation into the generative system, and designs the Diverse Conversation Generation (DCG) model. The model adopts a sentence function classifier trained independently to supervise the model output with modified loss function and back-propagation. The DCG model increases the diversity of output sentence, which could guide user to chat more with the system, extend the quality of conversation, and improve the user experience. The model is experimented on two different sequence-to sequence models, evaluated with perplexity and classify entropy, achieves better performance compared with two base models.

Diverse Conversation Generation System with Sentence Function Classification

Named entity recognition in the Indonesian language has significantly developed in recent years. However, it still lacks standardized publicly available corpora; a small dataset is available but suffers from inconsistent annotations. Therefore, we re-annotated the dataset to improve its consistency and benefit the community. Our re-annotation led to better training results from an effective baseline model consisting of bidirectional long short-term memory and conditional random fields. To fully utilize the limited available data, we utilized better contextualization and transferred external knowledge by exploiting monolingual and multilingual pre-trained language models, such as IndoBERT and XLM-RoBERTa. In addition to the general improvement from the language models, we observed that the monolingual model is more sensitive, while the multilingual ones show advantages in rich morphological knowledge. We also applied cross-lingual transfer learning to utilize high-resource corpora in other languages. We adopted English, Spanish, Dutch, and German as the source languages for the target Indonesian language and found that Dutch plays a special role in the data transfer method due to morphological similarity attributable to historical reasons.

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Dataset Enhancement and Multilingual Transfer for Named Entity Recognition in the Indonesian Language

In recent years, great success has been achieved in the field of natural language processing (NLP), thanks in part to the considerable amount of annotated resources. For named entity recognition (NER), most languages do not have such an abundance of labeled data as English, so the performances of those languages are relatively lower. To improve the performance, we propose a general approach called Back Attention Network (BAN). BAN uses a translation system to translate other language sentences into English and then applies a new mechanism named back attention knowledge transfer to obtain task-specific information from pre-trained high-resource languages NER model. This strategy can transfer high-layer features of well-trained model and enrich the semantic representations of the original language. Experiments on three different language datasets indicate that the proposed approach outperforms other state-of-the-art methods.

Back Attention Knowledge Transfer for Low-resource Named Entity Recognition.

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Back Attention Knowledge Transfer for Low-resource Named Entity Recognition.