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This paper studies the end-to-end construction of an NLP Knowledge Graph (KG) from scientific papers. We focus on extracting four types of relations: evaluatedOn between tasks and datasets, evaluatedBy between tasks and evaluation metrics, as well as coreferent and related relations between the same type of entities. For instance, F1-score is coreferent with F-measure. We introduce novel methods for each of these relation types and apply our final framework (SciNLP-KG) to 30,000 NLP papers from ACL Anthology to build a large-scale KG, which can facilitate automatically constructing scientific leaderboards for the NLP community. The results of our experiments indicate that the resulting KG contains high-quality information.
Entity linking - connecting entity mentions in a natural language utterance to knowledge graph (KG) entities is a crucial step for question answering over KGs. It is often based on measuring the string similarity between the entity label and its ment
End-to-end speech translation (ST), which directly translates from source language speech into target language text, has attracted intensive attentions in recent years. Compared to conventional pipeline systems, end-to-end ST models have advantages o
In this paper, we present Chinese lexical fusion recognition, a new task which could be regarded as one kind of coreference recognition. First, we introduce the task in detail, showing the relationship with coreference recognition and differences fro
In knowledge grounded conversation, domain knowledge plays an important role in a special domain such as Music. The response of knowledge grounded conversation might contain multiple answer entities or no entity at all. Although existing generative q
We present a graph-based Tree Adjoining Grammar (TAG) parser that uses BiLSTMs, highway connections, and character-level CNNs. Our best end-to-end parser, which jointly performs supertagging, POS tagging, and parsing, outperforms the previously repor