# Automatic Construction of Fine-Grained Paraphrase Corpora System Using Language Inference Model
## 2 Related Work  
Although the concept of paraphrase has been around for a long time, there has been no precise and widely accepted definition of paraphrase. The multiple definitions found in different literature can be viewed as \"paraphrases\" of each other. It is defined as \"approximate conceptual equivalence among outwardly different materials\" in an early linguistics text _Introduction to Text Linguistic_[2]. More recent literature from computational linguistics provided definitions such as \"expressing one thing in other words\" [3], or \"alternative ways to convey the same information\" [4]. Basically, paraphrases are sentences which convey the same or similar meanings. Although paraphrases, in a strict and narrow view, require completely semantically equivalence, most researchers take a broader view of paraphrase, allowing more flexibility and approximate equivalence known as 'quasi-paraphrase' [5]. The example from the [6] study shows that sentences 1 and 2 are considered as paraphrases even though they contain a slightly different amount of information. This, however, blurs the boundary between paraphrase and non-paraphrase, making a paraphrase processing system hard to build. 1. Authorities said a young man injured [PERSON]. 2. [PERSON] was hurt by a young man. The study of paraphrase can be useful for many natural language processing applications. In text summarisation, the information repeated across multiple documents is extracted through the identification of paraphrase sentences [7]. paraphrase identification can also be used in plagiarism detection. In natural language generation, the coherence and fluency improve when having more varied paraphrased candidate sentences. For example, paraphrase can be used to convert the professional terminology to simple text so that non-experts can understand [8]. It also allows questions with similar meaning to be expressed differently, which could improve system efficiency considerably. For example, in a question-answering system, a random user input question could be mapped to some of the frequently asked questions (FAQs) with the help of a paraphrase identification tool [9]. In addition, paraphrase identification can be used to recognise duplicate questions for online question and answer (QA) forums to combine and redirect similar questions. Other semantic relationships exists between sentences, among which, the entailment relation is closely related to paraphrase. Given two sentences, if a hypothesis sentence can be inferred from the given premise, we can say that the premise sentence entails the hypothesis sentence. For instance, sentence 1 entails sentence 2 in the example below. 1. **Premise**: A soccer game with multiple males playing. 2. **Hypothesis**: Some men are playing a sport. From the entailment relation perspective, paraphrase can be viewed as a special case of the entailment relationship where two sentences are bidirectionally entailed [10]. In other words, sentence A is a paraphrase of sentence B only if A entails B and B entails A. This perspective provides a solution for the paraphrase identification. However, in most cases, a paraphrase, as discussed in the previous section, is usually a quasi-paraphrase with a certain content loss. Limiting paraphrase to bidirectional entailment reduces a large proportion of cases [11]. Several studies had been aimed at extracting paraphrase data [3,4]. Microsoft Research Paraphrase Corpus (MRPC) is paraphrase corpus which is the most widely used benchmarks for paraphrase identification. It contains 5801 sentence pairs extracted from online news articles [1]. The benchmark itself only has one simple binary label indicating whether the pair is a paraphrase. The judgement depends on the rater's tolerance on the semantic divergence. The question-answer community Quora released Quora Question Pairs [12], which contains more than 400,000 question pairs. The question pairs sharing the same semantics are annotated as a duplicate based on the judgement of users and Quora's merging policy.