Verb Semantics for English-Chinese Translation

A common practice in operational Machine Translation (MT) and Natural Language Processing (NLP) systems is to assume that a verb has a fixed number of senses and rely on a precompiled lexicon to achieve large coverage. This paper demonstrates that this assumption is too weak to cope with the similar problems of lexical divergences between languages and unexpected uses of words that give rise to cases outside of the precompiled lexicon coverage. We first examine the lexical divergences between English verbs and Chinese verbs. We then focus on a specic lexical selection problem - translating English change-of-state verbs into Chinese verb compounds. We show that an accurate translation depends not only on information about the participants, but also on contextual information. Therefore, selectional restrictions on verb arguments lack the necessary power for accurate lexical selection. Second, we examine verb representation theories and practices in MT systems and show that under the fixed sense assumption, the existing representation schemes are not adequate for handling these lexical divergences and extending existing verb senses to unexpected usages. We then propose a method of verb representation based on conceptual lattices which allows the similarities among different verbs in different languages to be quantitatively measured. A prototype system UNICON implements this theory and performs more accurate MT lexical selection for our chosen set of verbs. An additional lexical module for UNICON is also provided that handles sense extension

On Yao's method of translation

Machine Translation, i.e., translating one kind of natural language to another kind of natural language by using a computer system, is a very important research branch in Artificial Intelligence. Yao developed a method of translation that he called ``Lexical-Semantic Driven". In his system he introduced 49 ``relation types" including case relations, event relations, semantic relations, and complex relations. The knowledge graph method is a new kind of method to represent an interlingua between natural languages. In this paper, we will give a comparison of these two methods. We will translate one Chinese sentence cited in Yao�s book by using these two methods. Finally, we will use the relations in knowledge graph theory to represent the ``relations" in Lexical-Semantic Driven, and partition the relations in Lexical-Semantic Driven into groups according to the relations in knowledge graph theory

Translation: an example from ancient Chinese to modern Chinese

In this paper, we gave an idea of translation by means of knowledge graph theory from ancient Chinese to modern Chinese, by using an example story. Actually, we give the details of the method of translation from ancient Chinese to modern Chinese step by step as carried out by hand. From the example, we found that knowledge graphs have a strong ability to represent sentences. And we also found that there are things that should be discussed if we want to translate automatically from one kind of language to another kind of language by using knowledge graph theory

Middle voice marking in Tibeto-Burman

Middle voice marking is very rarely recognized as such in the grammars written on Tibeto-Burman languages. It is often simply treated as a normal direct reflexive or as an intransitivizer. In order to draw the attention of scholars to the existence and function of middle voice marking in Tibeto-Burman languages, the present paper discusses the form and function of middle marking in several of these languages. We will first discuss key facts about middle marking in general, then discuss the individual Tibeto-Burman examples

A preliminary bibliography on focus

[I]n its present form, the bibliography contains approximately 1100 entries. Bibliographical work is never complete, and the present one is still modest in a number of respects. It is not annotated, and it still contains a lot of mistakes and inconsistencies. It has nevertheless reached a stage which justifies considering the possibility of making it available to the public. The first step towards this is its pre-publication in the form of this working paper. […] The bibliography is less complete for earlier years. For works before 1970, the bibliographies of Firbas and Golkova 1975 and Tyl 1970 may be consulted, which have not been included here

MultiMWE: building a multi-lingual multi-word expression (MWE) parallel corpora

Multi-word expressions (MWEs) are a hot topic in research in natural language processing (NLP), including topics such as MWE detection, MWE decomposition, and research investigating the exploitation of MWEs in other NLP fields such as Machine Translation. However, the availability of bilingual or multi-lingual MWE corpora is very limited. The only bilingual MWE corpora that we are aware of is from the PARSEME (PARSing and Multi-word Expressions) EU project. This is a small collection of only 871 pairs of English-German MWEs. In this paper, we present multi-lingual and bilingual MWE corpora that we have extracted from root parallel corpora. Our collections are 3,159,226 and 143,042 bilingual MWE pairs for German-English and Chinese-English respectively after filtering. We examine the quality of these extracted bilingual MWEs in MT experiments. Our initial experiments applying MWEs in MT show improved translation performances on MWE terms in qualitative analysis and better general evaluation scores in quantitative analysis, on both German-English and Chinese-English language pairs. We follow a standard experimental pipeline to create our MultiMWE corpora which are available online. Researchers can use this free corpus for their own models or use them in a knowledge base as model features