Given a controversial target such as ``nuclear energy'', argument mining aims
to identify the argumentative text from heterogeneous sources. Current
approaches focus on exploring better ways of integrating the target-associated
semantic information with the argumentative text. Despite their empirical
successes, two issues remain unsolved: (i) a target is represented by a word or
a phrase, which is insufficient to cover a diverse set of target-related
subtopics; (ii) the sentence-level topic information within an argument, which
we believe is crucial for argument mining, is ignored. To tackle the above
issues, we propose a novel explainable topic-enhanced argument mining approach.
Specifically, with the use of the neural topic model and the language model,
the target information is augmented by explainable topic representations.
Moreover, the sentence-level topic information within the argument is captured
by minimizing the distance between its latent topic distribution and its
semantic representation through mutual learning. Experiments have been
conducted on the benchmark dataset in both the in-target setting and the
cross-target setting. Results demonstrate the superiority of the proposed model
against the state-of-the-art baselines.

提出一种新颖的可解释的主题增强的论证挖掘方法，通过使用神经主题模型和语言模型，在异构来源的数据中识别与核能相关的论证文本，并捕捉论证中句子级的主题信息。实验结果表明，该模型相比现有基准模型具有优越性。

可解释的主题增强异构来源的论点挖掘

Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources

Significant advances have been made in recent years on Natural Language
Processing with machines surpassing human performance in many tasks, including
but not limited to Question Answering. The majority of deep learning methods
for Question Answering targets domains with large datasets and highly matured
literature. The area of Nuclear and Atomic energy has largely remained
unexplored in exploiting non-annotated data for driving industry viable
applications. Due to lack of dataset, a new dataset was created from the 7000
research papers on nuclear domain. This paper contributes to research in
understanding nuclear domain knowledge which is then evaluated on Nuclear
Question Answering Dataset (NQuAD) created by nuclear domain experts as part of
this research. NQuAD contains 612 questions developed on 181 paragraphs
randomly selected from the IGCAR research paper corpus. In this paper, the
Nuclear Bidirectional Encoder Representational Transformers (NukeBERT) is
proposed, which incorporates a novel technique for building BERT vocabulary to
make it suitable for tasks with less training data. The experiments evaluated
on NQuAD revealed that NukeBERT was able to outperform BERT significantly, thus
validating the adopted methodology. Training NukeBERT is computationally
expensive and hence we will be open-sourcing the NukeBERT pretrained weights
and NQuAD for fostering further research work in the nuclear domain.

该文提出了一种基于 Bidirectional Encoder Representational Transformers (BERT) 技术的新型预训练模型 NukeBERT，能够有效地提高核能领域问题回答的准确性，并通过开源数据集 NQuAD 促进相关领域的进一步研究。