Techniques for generating and recognizing paraphrases, i.e., semantically equivalent expressions, play an important role in a wide range of natural language processing tasks. In the last decade, the task of automatic acquisition of subsentential paraphrases, i.e., words and phrases with (approximately) the same meaning, has been drawing much attention in the research community. The core problem is to obtain paraphrases of high quality in large quantity. This article presents a method for tackling this issue by systematically expanding an initial seed lexicon made up of high-quality paraphrases. This involves automatically capturing morpho-semantic and syntactic generalizations within the lexicon and using them to leverage the power of large-scale monolingual data. Given an input set of paraphrases, our method starts by inducing paraphrase patterns that constitute generalizations over corresponding pairs of lexical variants, such as “amending” and “amendment,” in a fully empirical way. It then searches large-scale monolingual data for new paraphrases matching those patterns. The results of our experiments on English, French, and Japanese demonstrate that our method manages to expand seed lexicons by a large multiple. Human evaluation based on paraphrase substitution tests reveals that the automatically acquired paraphrases are also of high quality.

Neural machine translation represents an exciting leap forward in translation quality.But what longstanding weaknesses does it resolve, and which remain?We address these questions with a challenge set approach to translation evaluation and error analysis.A challenge set consists of a small set of sentences, each hand-designed to probe a system's capacity to bridge a particular structural divergence between languages.To exemplify this approach, we present an English-French challenge set, and use it to analyze phrase-based and neural systems.The resulting analysis provides not only a more fine-grained picture of the strengths of neural systems, but also insight into which linguistic phenomena remain out of reach.

This study tackles the problem of paraphrase acquisition: achieving high coverage as well as accuracy. Our method first induces paraphrase patterns from given seed paraphrases, exploiting the generality of paraphrases exhibited by pairs of lexical variants, e.g., “amendment” and “amending,” in a fully empirical way. It then searches monolingual corpora for new paraphrases that match the patterns. This can extract paraphrases comprising words that are completely different from those of the given seeds. In experiments, our method expanded seed sets by factors of 42 to 206, gaining 84% to 208% more coverage than a previous method that generalizes only identical word forms. Human evaluation through a paraphrase substitution test demonstrated that the newly acquired paraphrases retained reasonable quality, given substantially high-quality seeds

Translation is a key capability to access relevant information expressed in various languages on social media. Unfortunately, systematically translating all content far exceeds the capacity of most organizations. Computer-aided translation (CAT) tools can significantly increase the productivity of translators, but can not ultimately cope with the overwhelming amount of content to translate. In this contribution, we describe and experiment with an approach where we use the structure in a corpus to adequately route the content to the proper workflow, including translators, CAT tools or purely automatic approaches. We show that linguistically motivated structure such as document genre can help decide on the proper translation workflow. However, automatically discovered structure has an effect that is at least as important and allows us to define groups of documents that may be translated automatically with reasonable output quality. This suggests that computational intelligence models that can efficiently organize document collection will provide increased capability to access textual content from various target languages.

This paper describes the use of a probabilistic translation model to cross-language IR (CLIR). The performance of this approach is compared with that using machine translation (MT). It is shown that using a probabilistic model, we are able to obtain performances close to those using an MT system. In addition, we also investigated the possibility of automatically gather parallel texts from the Web in an attempt to construct a reasonable training corpus. The result is very encouraging. We showed that in several tests, such a training corpus is as good as a manually constructed one for CLIR purposes.

This paper describes a system designed for use by professional translators that enables them to dictate their translation. Because the speech recognizer has access to the source text as well as the spoken translation, a statistical translation model can guide recognition. This can be done in many different ways---which is best? We discuss the experiments that led to integration of the translation model in a way that improves both speed and performance. 1 Introduction The TransTalk project attempts to integrate speech recognition and machine translation in a way that makes maximal use of their complementary strengths. Professional translators often dictate their translations first and have them typed afterwards. If they dictate to a speech recognition system instead, and if that system has access to the source language text, it can use probabilistic translation models to aid recognition. For instance, if the speech recognition system is deciding between the acoustically similar French ...

Professional translators often dictate their translations orally and have them typed afterwards. The TransTalk project aims at automating the second part of this process. Its originality as a dictation system lies in the fact that both the acoustic signal produced by the translator and the source text under translation are made available to the system. Probable translations of the source text can be predicted and these predictions used to help the speech recognition system in its lexical choices. We present the results of the first prototype, which show a marked improvement in the performance of the speech recognition task when translation predictions are taken into account. 1 Introduction The integration of machine translation and speech technology is currently the focus of major projects in several countries [5, 6, 9]. Usually, the aim of these efforts is some type of speech-to-speech translation, where speech recognition, machine translation and speech synthesis are performed seque...

We present a progress report on our research on nominal compounds (NC's). Recent approaches to this problem in linguistics and natural language processing (NLP) are reviewed and criticized. We argue that the notion of "role nominal", which is at the interface of linguistic and extralinguistic knowledge, is crucial for characterizing NC's as well as other linguistic phenomena. We examine a number of constraints on the semantic interpretation rules for NC's. Proposals are made that should improve the capability of NLP systems to deal with NC's.