The Fact Extraction and VERification (FEVER) shared task was launched to
support the development of systems able to verify claims by extracting
supporting or refuting facts from raw text. The shared task organizers provide
a large-scale dataset for the consecutive steps involved in claim verification,
in particular, document retrieval, fact extraction, and claim classification.
In this paper, we present our claim verification pipeline approach, which,
according to the preliminary results, scored third in the shared task, out of
23 competing systems. For the document retrieval, we implemented a new entity
linking approach. In order to be able to rank candidate facts and classify a
claim on the basis of several selected facts, we introduce two extensions to
the Enhanced LSTM (ESIM).

本文提出了一个用于验证索赔的管线方法，在文档检索中使用了一种新的实体链接方法，并引入了两个 Enhanced LSTM (ESIM) 的扩展来对索取的事实进行排名并对索赔进行分类，方法得分第三。

UKP-Athene: 面向声明验证的多句文本蕴涵

UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification

Ubuntu dialogue corpus is the largest public available dialogue corpus to
make it feasible to build end-to-end deep neural network models directly from
the conversation data. One challenge of Ubuntu dialogue corpus is the large
number of out-of-vocabulary words. In this paper we proposed a method which
combines the general pre-trained word embedding vectors with those generated on
the task-specific training set to address this issue. We integrated character
embedding into Chen et al's Enhanced LSTM method (ESIM) and used it to evaluate
the effectiveness of our proposed method. For the task of next utterance
selection, the proposed method has demonstrated a significant performance
improvement against original ESIM and the new model has achieved
state-of-the-art results on both Ubuntu dialogue corpus and Douban conversation
corpus. In addition, we investigated the performance impact of end-of-utterance
and end-of-turn token tags.

该论文提出了一种方法，将通用的预训练词向量与特定任务的训练集生成的词向量相结合，解决 Ubuntu 对话语料库中的大量生僻词问题，应用于下一个话语选择任务中，结果表明该方法优于原始方法 ESIM，并在 Ubuntu 和 Douban 对话语料库上取得了最先进的结果。