Examining the Tip of the Iceberg: A Data Set for Idiom Translation

Neural Machine Translation (NMT) has been widely used in recent years with significant improvements for many language pairs. Although state-of-the-art NMT systems are generating progressively better translations, idiom translation remains one of the open challenges in this field. Idioms, a category of multiword expressions, are an interesting language phenomenon where the overall meaning of the expression cannot be composed from the meanings of its parts. A first important challenge is the lack of dedicated data sets for learning and evaluating idiom translation. In this paper we address this problem by creating the first large-scale data set for idiom translation. Our data set is automatically extracted from a widely used German-English translation corpus and includes, for each language direction, a targeted evaluation set where all sentences contain idioms and a regular training corpus where sentences including idioms are marked. We release this data set and use it to perform preliminary NMT experiments as the first step towards better idiom translation.Comment: Accepted at LREC 201

Multiword expression processing: A survey

Multiword expressions (MWEs) are a class of linguistic forms spanning conventional word boundaries that are both idiosyncratic and pervasive across different languages. The structure of linguistic processing that depends on the clear distinction between words and phrases has to be re-thought to accommodate MWEs. The issue of MWE handling is crucial for NLP applications, where it raises a number of challenges. The emergence of solutions in the absence of guiding principles motivates this survey, whose aim is not only to provide a focused review of MWE processing, but also to clarify the nature of interactions between MWE processing and downstream applications. We propose a conceptual framework within which challenges and research contributions can be positioned. It offers a shared understanding of what is meant by "MWE processing," distinguishing the subtasks of MWE discovery and identification. It also elucidates the interactions between MWE processing and two use cases: Parsing and machine translation. Many of the approaches in the literature can be differentiated according to how MWE processing is timed with respect to underlying use cases. We discuss how such orchestration choices affect the scope of MWE-aware systems. For each of the two MWE processing subtasks and for each of the two use cases, we conclude on open issues and research perspectives

Representation and parsing of multiword expressions

This book consists of contributions related to the definition, representation and parsing of MWEs. These reflect current trends in the representation and processing of MWEs. They cover various categories of MWEs such as verbal, adverbial and nominal MWEs, various linguistic frameworks (e.g. tree-based and unification-based grammars), various languages including English, French, Modern Greek, Hebrew, Norwegian), and various applications (namely MWE detection, parsing, automatic translation) using both symbolic and statistical approaches

Current trends

Deep parsing is the fundamental process aiming at the representation of the syntactic structure of phrases and sentences. In the traditional methodology this process is based on lexicons and grammars representing roughly properties of words and interactions of words and structures in sentences. Several linguistic frameworks, such as Headdriven Phrase Structure Grammar (HPSG), Lexical Functional Grammar (LFG), Tree Adjoining Grammar (TAG), Combinatory Categorial Grammar (CCG), etc., offer different structures and combining operations for building grammar rules. These already contain mechanisms for expressing properties of Multiword Expressions (MWE), which, however, need improvement in how they account for idiosyncrasies of MWEs on the one hand and their similarities to regular structures on the other hand. This collaborative book constitutes a survey on various attempts at representing and parsing MWEs in the context of linguistic theories and applications

Machine translation of non-contiguous multiword units

Non-adjacent linguistic phenomena such as non-contiguous multiwords and other phrasal units containing insertions, i.e., words that are not part of the unit, are difficult to process and remain a problem for NLP applications. Non-contiguous multiword units are common across languages and constitute some of the most important challenges to high quality machine translation. This paper presents an empirical analysis of non-contiguous multiwords, and highlights our use of the Logos Model and the Semtab function to deploy semantic knowledge to align non-contiguous multiword units with the goal to translate these units with high fidelity. The phrase level manual alignments illustrated in the paper were produced with the CLUE-Aligner, a Cross-Language Unit Elicitation alignment tool.info:eu-repo/semantics/acceptedVersio

Understanding and Enhancing the Use of Context for Machine Translation

To understand and infer meaning in language, neural models have to learn complicated nuances. Discovering distinctive linguistic phenomena from data is not an easy task. For instance, lexical ambiguity is a fundamental feature of language which is challenging to learn. Even more prominently, inferring the meaning of rare and unseen lexical units is difficult with neural networks. Meaning is often determined from context. With context, languages allow meaning to be conveyed even when the specific words used are not known by the reader. To model this learning process, a system has to learn from a few instances in context and be able to generalize well to unseen cases. The learning process is hindered when training data is scarce for a task. Even with sufficient data, learning patterns for the long tail of the lexical distribution is challenging. In this thesis, we focus on understanding certain potentials of contexts in neural models and design augmentation models to benefit from them. We focus on machine translation as an important instance of the more general language understanding problem. To translate from a source language to a target language, a neural model has to understand the meaning of constituents in the provided context and generate constituents with the same meanings in the target language. This task accentuates the value of capturing nuances of language and the necessity of generalization from few observations. The main problem we study in this thesis is what neural machine translation models learn from data and how we can devise more focused contexts to enhance this learning. Looking more in-depth into the role of context and the impact of data on learning models is essential to advance the NLP field. Moreover, it helps highlight the vulnerabilities of current neural networks and provides insights into designing more robust models.Comment: PhD dissertation defended on November 10th, 202

Representations of Idioms for Natural Language Processing: Idiom type and token identification, Language Modelling and Neural Machine Translation

An idiom is a multiword expression (MWE) whose meaning is non- compositional, i.e., the meaning of the expression is different from the meaning of its individual components. Idioms are complex construc- tions of language used creatively across almost all text genres. Idioms pose problems to natural language processing (NLP) systems due to their non-compositional nature, and the correct processing of idioms can improve a wide range of NLP systems. Current approaches to idiom processing vary in terms of the amount of discourse history required to extract the features necessary to build representations for the expressions. These features are, in general, stat- istics extracted from the text and often fail to capture all the nuances involved in idiom usage. We argue in this thesis that a more flexible representations must be used to process idioms in a range of idiom related tasks. We demonstrate that high-dimensional representations allow idiom classifiers to better model the interactions between global and local features and thereby improve the performance of these systems with regard to processing idioms. In support of this thesis we demonstrate that distributed representations of sentences, such as those generated by a Recurrent Neural Network (RNN) greatly reduce the amount of discourse history required to process idioms and that by using those representations a “general” classifier, that can take any expression as input and classify it as either an idiomatic or literal usage, is feasible. We also propose and evaluate a novel technique to add an attention module to a language model in order to bring forward past information in a RNN-based Language Model (RNN-LM). The results of our evaluation experiments demonstrate that this attention module increases the performance of such models in terms of the perplexity achieved when processing idioms. Our analysis also shows that it improves the performance of RNN-LMs on literal language and, at the same time, helps to bridge long-distance dependencies and reduce the number of parameters required in RNN-LMs to achieve state-of-the-art performance. We investigate the adaptation of this novel RNN-LM to Neural Machine Translation (NMT) systems and we show that, despite the mixed results, it improves the translation of idioms into languages that require distant reordering such as German. We also show that these models are suited to small corpora for in-domain translations for language pairs such as English/Brazilian-Portuguese

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT