# Contradiction Detection with Contradiction-Specific Word Embedding
## 1 Introduction  
Contradiction is a kind of semantic relation between sentences. Contradiction occurs when sentences are unlikely to be correct at the same time [1]. For example, the contradiction happens between the sentence pair \"Some people and vehicles are on a crowded street\" and \"Some people and vehicles are on an empty street\". Contradiction detection aims to recognize the contrasting meanings between two sentences [2]. Contradiction detection is helpful in many fields of natural language processing, such as information integration [3], inconsistency discovery [4, 5] and sarcasm detection [6]. Contradiction detection can be regarded as a classification problem. Traditional approaches build classifiers and design effective features to improve classification accuracy. However, feature designing relies on professional knowledge and hardly captures latent semantic features. For the contradiction detection task, an effective feature learning approach is to learn the semantic relation representation from input texts. The representation learning of semantic relation is based on word embedding. However, word embedding is challenging to employ in contradiction detection because of the distributional semantics hypothesis. Traditional context-based word embedding learning algorithms typically map words with similar contexts into closed vectors. That means the words with contrasting meanings will be mapped into close vectors as long as they share a similar context [7, 8], as shown in Figure 0(a). The close vectors of contrasting words will lead to similar representation of contradictory sentences, as shown in Figure 0(b). For a contradiction detection task, the ideal situation is that the vectors of contrasting words remain separate from each other, as shown in Figure 1(a). The appropriate mapping of contrasting words will lead to a high discrimination of contradictory sentences, as shown in Figure 1(b). To address the issue, there are some recent methods [7; 8; 9; 10] currently being investigated. [PERSON] et al. [8] apply lexical knowledge such as WordNet [11] and PPDB [12] (Paraphrase Database) ([[http://www.cis.upenn.edu/~ccb/ppdb/](http://www.cis.upenn.edu/~ccb/ppdb/)]([http://www.cis.upenn.edu/~ccb/ppdb/](http://www.cis.upenn.edu/~ccb/ppdb/))) to revise word embedding by using 12,802 antonym pairs and 31,828 synonymy pairs. The methods of [PERSON] et al. [7] and [PERSON] et al. [10] use WordNet and Thesaurus to get more antonym pairs and synonym pairs as the semantic constraints. These methods can get exact antonym pairs in these lexical resources; however, the number of antonym pairs is limited. In addition, antonym pairs are just part of contrasting word pairs, and many other contrasting word pairs such as \"shelve\" and \"pass\" can not be obtained from the lexical resources. [PERSON] et al. [9] use patterns such as \"from X to Y\" and \"either X or Y\" to extract antonyms from Wikipedia. However, the pattern based methods would meet the data sparsity problem. In the contradiction detection task, the pairs of sentences with contradiction relations always own contrasting words which are hardly discriminated by using antonym-based word embedding learning algorithms. We present a method to construct a large corpus of contrasting pairs, including word pairs and phrase pairs. The large-scale corpus of contrasting pairs are generated from PPDB [12] and WordNet [11] automatically. Through our method, we obtain 1.9 million contrasting pairs and 1.6 million paraphrase pairs, that are one hundred times the size of the corpus [PERSON] et al. [8] used. Although the automatically-generated corpus contains noises, we argue that it is effective enough to be leveraged as task-specific supervisions to learn CWE. Based on the corpus, we develop a neural network to learn contradiction-specific word embedding (CWE). In the aim of separating contrasting words in an embedding space, CWE is learnt based on the pairs with paraphrase relation or contradiction relation.