Stance detection (SD) can be considered a special case of textual entailment
recognition (TER), a generic natural language task. Modelling SD as TER may
offer benefits like more training data and a more general learning scheme. In
this paper, we present an initial empirical analysis of this approach. We apply
it to a difficult but relevant test case where no existing labelled SD dataset
is available, because this is where modelling SD as TER may be especially
helpful. We also leverage measurement knowledge from social sciences to improve
model performance. We discuss our findings and suggest future research
directions.

本文通过实证分析探讨将立场检测问题建模为通用的文本蕴涵识别任务的可能性，并利用社会科学的测量知识提高模型性能。

将立场检测建模为文本蕴涵识别，并利用社会科学的测量知识

Modelling Stance Detection as Textual Entailment Recognition and Leveraging Measurement Knowledge from Social Sciences

Textual entailment recognition is one of the basic natural language
understanding(NLU) tasks. Understanding the meaning of sentences is a
prerequisite before applying any natural language processing(NLP) techniques to
automatically recognize the textual entailment. A text entails a hypothesis if
and only if the true value of the hypothesis follows the text. Classical
approaches generally utilize the feature value of each word from word embedding
to represent the sentences. In this paper, we propose a novel approach to
identifying the textual entailment relationship between text and hypothesis,
thereby introducing a new semantic feature focusing on empirical
threshold-based semantic text representation. We employ an element-wise
Manhattan distance vector-based feature that can identify the semantic
entailment relationship between the text-hypothesis pair. We carried out
several experiments on a benchmark entailment classification(SICK-RTE) dataset.
We train several machine learning(ML) algorithms applying both semantic and
lexical features to classify the text-hypothesis pair as entailment, neutral,
or contradiction. Our empirical sentence representation technique enriches the
semantic information of the texts and hypotheses found to be more efficient
than the classical ones. In the end, our approach significantly outperforms
known methods in understanding the meaning of the sentences for the textual
entailment classification task.

本文提出了一种新的基于阈值的语义文本表示方法，并结合机器学习算法应用语义和词汇特征对文本 - 假设对进行分类，该技术有效地丰富了文本的语义信息，在文字蕴涵分类任务中在意义理解方面的表现明显优于已知方法

通过经验文本表示的语义特征实现文本蕴涵识别

Textual Entailment Recognition with Semantic Features from Empirical Text Representation

In this paper, we describe DeFactoNLP, the system we designed for the FEVER
2018 Shared Task. The aim of this task was to conceive a system that can not
only automatically assess the veracity of a claim but also retrieve evidence
supporting this assessment from Wikipedia. In our approach, the Wikipedia
documents whose Term Frequency-Inverse Document Frequency (TFIDF) vectors are
most similar to the vector of the claim and those documents whose names are
similar to those of the named entities (NEs) mentioned in the claim are
identified as the documents which might contain evidence. The sentences in
these documents are then supplied to a textual entailment recognition module.
This module calculates the probability of each sentence supporting the claim,
contradicting the claim or not providing any relevant information to assess the
veracity of the claim. Various features computed using these probabilities are
finally used by a Random Forest classifier to determine the overall
truthfulness of the claim. The sentences which support this classification are
returned as evidence. Our approach achieved a 0.4277 evidence F1-score, a
0.5136 label accuracy and a 0.3833 FEVER score.

本文描述了 DeFactoNLP 系统，该系统用于自动评估声明的准确性并从维基百科中检索支持此评估的证据。我们的方法基于 TFIDF 向量和被提出的命名实体来识别包含证据的维基百科文章，并通过文本蕴涵识别模块来计算句子支持声明的概率。我们的方法在 FEVER 2018 共享任务中获得了不错的表现。