D6.2 Integrated Final Version of the Components for Lexical Acquisition

The PANACEA project has addressed one of the most critical bottlenecks that threaten the development of technologies to support multilingualism in Europe, and to process the huge quantity of multilingual data produced annually. Any attempt at automated language processing, particularly Machine Translation (MT), depends on the availability of language-specific resources. Such Language Resources (LR) contain information about the language\u27s lexicon, i.e. the words of the language and the characteristics of their use. In Natural Language Processing (NLP), LRs contribute information about the syntactic and semantic behaviour of words - i.e. their grammar and their meaning - which inform downstream applications such as MT. To date, many LRs have been generated by hand, requiring significant manual labour from linguistic experts. However, proceeding manually, it is impossible to supply LRs for every possible pair of European languages, textual domain, and genre, which are needed by MT developers. Moreover, an LR for a given language can never be considered complete nor final because of the characteristics of natural language, which continually undergoes changes, especially spurred on by the emergence of new knowledge domains and new technologies. PANACEA has addressed this challenge by building a factory of LRs that progressively automates the stages involved in the acquisition, production, updating and maintenance of LRs required by MT systems. The existence of such a factory will significantly cut down the cost, time and human effort required to build LRs. WP6 has addressed the lexical acquisition component of the LR factory, that is, the techniques for automated extraction of key lexical information from texts, and the automatic collation of lexical information into LRs in a standardized format. The goal of WP6 has been to take existing techniques capable of acquiring syntactic and semantic information from corpus data, improving upon them, adapting and applying them to multiple languages, and turning them into powerful and flexible techniques capable of supporting massive applications. One focus for improving the scalability and portability of lexical acquisition techniques has been to extend exiting techniques with more powerful, less "supervised" methods. In NLP, the amount of supervision refers to the amount of manual annotation which must be applied to a text corpus before machine learning or other techniques are applied to the data to compile a lexicon. More manual annotation means more accurate training data, and thus a more accurate LR. However, given that it is impractical from a cost and time perspective to manually annotate the vast amounts of data required for multilingual MT across domains, it is important to develop techniques which can learn from corpora with less supervision. Less supervised methods are capable of supporting both large-scale acquisition and efficient domain adaptation, even in the domains where data is scarce. Another focus of lexical acquisition in PANACEA has been the need of LR users to tune the accuracy level of LRs. Some applications may require increased precision, or accuracy, where the application requires a high degree of confidence in the lexical information used. At other times a greater level of coverage may be required, with information about more words at the expense of some degree of accuracy. Lexical acquisition in PANACEA has investigated confidence thresholds for lexical acquisition to ensure that the ultimate users of LRs can generate lexical data from the PANACEA factory at the desired level of accuracy

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

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

Wide-coverage deep statistical parsing using automatic dependency structure annotation

A number of researchers (Lin 1995; Carroll, Briscoe, and Sanfilippo 1998; Carroll et al. 2002; Clark and Hockenmaier 2002; King et al. 2003; Preiss 2003; Kaplan et al. 2004;Miyao and Tsujii 2004) have convincingly argued for the use of dependency (rather than CFG-tree) representations for parser evaluation. Preiss (2003) and Kaplan et al. (2004) conducted a number of experiments comparing “deep” hand-crafted wide-coverage with “shallow” treebank- and machine-learning based parsers at the level of dependencies, using simple and automatic methods to convert tree output generated by the shallow parsers into dependencies. In this article, we revisit the experiments in Preiss (2003) and Kaplan et al. (2004), this time using the sophisticated automatic LFG f-structure annotation methodologies of Cahill et al. (2002b, 2004) and Burke (2006), with surprising results. We compare various PCFG and history-based parsers (based on Collins, 1999; Charniak, 2000; Bikel, 2002) to find a baseline parsing system that fits best into our automatic dependency structure annotation technique. This combined system of syntactic parser and dependency structure annotation is compared to two hand-crafted, deep constraint-based parsers (Carroll and Briscoe 2002; Riezler et al. 2002). We evaluate using dependency-based gold standards (DCU 105, PARC 700, CBS 500 and dependencies for WSJ Section 22) and use the Approximate Randomization Test (Noreen 1989) to test the statistical significance of the results. Our experiments show that machine-learning-based shallow grammars augmented with sophisticated automatic dependency annotation technology outperform hand-crafted, deep, widecoverage constraint grammars. Currently our best system achieves an f-score of 82.73% against the PARC 700 Dependency Bank (King et al. 2003), a statistically significant improvement of 2.18%over the most recent results of 80.55%for the hand-crafted LFG grammar and XLE parsing system of Riezler et al. (2002), and an f-score of 80.23% against the CBS 500 Dependency Bank (Carroll, Briscoe, and Sanfilippo 1998), a statistically significant 3.66% improvement over the 76.57% achieved by the hand-crafted RASP grammar and parsing system of Carroll and Briscoe (2002)

Generating Tailored, Comparative Descriptions with Contextually Appropriate Intonation

Generating responses that take user preferences into account requires adaptation at all levels of the generation process. This article describes a multi-level approach to presenting user-tailored information in spoken dialogues which brings together for the first time multi-attribute decision models, strategic content planning, surface realization that incorporates prosody prediction, and unit selection synthesis that takes the resulting prosodic structure into account. The system selects the most important options to mention and the attributes that are most relevant to choosing between them, based on the user model. Multiple options are selected when each offers a compelling trade-off. To convey these trade-offs, the system employs a novel presentation strategy which straightforwardly lends itself to the determination of information structure, as well as the contents of referring expressions. During surface realization, the prosodic structure is derived from the information structure using Combinatory Categorial Grammar in a way that allows phrase boundaries to be determined in a flexible, data-driven fashion. This approach to choosing pitch accents and edge tones is shown to yield prosodic structures with significantly higher acceptability than baseline prosody prediction models in an expert evaluation. These prosodic structures are then shown to enable perceptibly more natural synthesis using a unit selection voice that aims to produce the target tunes, in comparison to two baseline synthetic voices. An expert evaluation and f0 analysis confirm the superiority of the generator-driven intonation and its contribution to listeners' ratings

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Trustworthy Formal Natural Language Specifications

Interactive proof assistants are computer programs carefully constructed to check a human-designed proof of a mathematical claim with high confidence in the implementation. However, this only validates truth of a formal claim, which may have been mistranslated from a claim made in natural language. This is especially problematic when using proof assistants to formally verify the correctness of software with respect to a natural language specification. The translation from informal to formal remains a challenging, time-consuming process that is difficult to audit for correctness. This paper shows that it is possible to build support for specifications written in expressive subsets of natural language, within existing proof assistants, consistent with the principles used to establish trust and auditability in proof assistants themselves. We implement a means to provide specifications in a modularly extensible formal subset of English, and have them automatically translated into formal claims, entirely within the Lean proof assistant. Our approach is extensible (placing no permanent restrictions on grammatical structure), modular (allowing information about new words to be distributed alongside libraries), and produces proof certificates explaining how each word was interpreted and how the sentence's structure was used to compute the meaning. We apply our prototype to the translation of various English descriptions of formal specifications from a popular textbook into Lean formalizations; all can be translated correctly with a modest lexicon with only minor modifications related to lexicon size.Comment: arXiv admin note: substantial text overlap with arXiv:2205.0781