Translation studies rely more and more on corpus data to examine specificities of translated texts, that can be translated from different original languages and compared to original texts. In parallel, more and more multilingual corpora are becoming available for various natural language processing tasks. This paper questions the use of these multilingual corpora in translation studies and show...

We introduce a simple and effective method to learn discourse-specific word embeddings (DSWE) for implicit discourse relation recognition. Specifically, DSWE is learned by performing connective classification on massive explicit discourse data, and capable of capturing discourse relationships between words. On the PDTB data set, using DSWE as features achieves significant improvements over base...

Translation of discourse relations is one of the recent efforts of incorporating discourse information to statistical machine translation (SMT). While existing works focus on disambiguation of ambiguous discourse connectives, or transformation of discourse trees, only explicit discourse relations are tackled. A greater challenge exists in machine translation of Chinese, since implicit discourse...

It is a widely accepted fact that coherence enables a text’s comprehensibility. A major source of coherence is discourse cohesion (textual properties of the text). Lexical cohesion (e.g. synonymy) and discourse connectives are two major types of discourse cohesion. We investigate the contribution of these two types of cohesion to the overall comprehension of bi-clausal sentences in Turkish. In ...

Discourse relations are a bridge between sentence-level semantics and discourselevel semantics. They can be signalled explicitly with discourse connectives or conveyed implicitly, to be inferred by a comprehender. The same discourse units can be related in more than one way, signalled by multiple connectives. But multiple connectives aren’t necessary: Multiple relations can be conveyed even whe...

This paper presents a mutual learning method using hierarchical deep semantics for the classification of implicit discourse relations in English. With the absence of explicit discourse markers, traditional discourse techniques mainly concentrate on discrete linguistic features in this task, which always leads to data sparse problem. To relieve this problem, we propose a mutual learning neural m...

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We present a new resource, the UnixMan Corpus, for studying language learning it the domain of Unix utility manuals. The corpus is built by mining Unix (and other Unix related) man pages for parallel example entries, consisting of English textual descriptions with corresponding command examples. The commands provide a grounded and ambiguous semantics for the textual descriptions, making the cor...

This paper describes a classifier that assigns semantic thesaurus categories to unknown Chinese words (words not already in the CiLin thesaurus and the Chinese Electronic Dictionary, but in the Sinica Corpus). The focus of the paper differs in two ways from previous research in this particular area. Prior research in Chinese unknown words mostly focused on proper nouns (Lee 1993, Lee, Lee and C...

Annotation studies in CL are generally unscientific: they are mostly not reproducible, make use of too few (and often non-independent) annotators and use guidelines that are often something of a moving target. Additionally, the notion of ‘expert annotators’ invariably means only that the annotators have linguistic training. While this can be acceptable in some special contexts, it is often far ...