Selecting and Generating Computational Meaning Representations for Short Texts

Language conveys meaning, so natural language processing (NLP) requires representations of meaning. This work addresses two broad questions: (1) What meaning representation should we use? and (2) How can we transform text to our chosen meaning representation? In the first part, we explore different meaning representations (MRs) of short texts, ranging from surface forms to deep-learning-based models. We show the advantages and disadvantages of a variety of MRs for summarization, paraphrase detection, and clustering. In the second part, we use SQL as a running example for an in-depth look at how we can parse text into our chosen MR. We examine the text-to-SQL problem from three perspectives—methodology, systems, and applications—and show how each contributes to a fuller understanding of the task.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143967/1/cfdollak_1.pd

New resources and ideas for semantic parser induction

In this thesis, we investigate the general topic of computational natural language understanding (NLU), which has as its goal the development of algorithms and other computational methods that support reasoning about natural language by the computer. Under the classical approach, NLU models work similar to computer compilers (Aho et al., 1986), and include as a central component a semantic parser that translates natural language input (i.e., the compiler’s high-level language) to lower-level formal languages that facilitate program execution and exact reasoning. Given the difficulty of building natural language compilers by hand, recent work has centered around semantic parser induction, or on using machine learning to learn semantic parsers and semantic representations from parallel data consisting of example text-meaning pairs (Mooney, 2007a). One inherent difficulty in this data-driven approach is finding the parallel data needed to train the target semantic parsing models, given that such data does not occur naturally “in the wild” (Halevy et al., 2009). Even when data is available, the amount of domain- and language-specific data and the nature of the available annotations might be insufficient for robust machine learning and capturing the full range of NLU phenomena. Given these underlying resource issues, the semantic parsing field is in constant need of new resources and datasets, as well as novel learning techniques and task evaluations that make models more robust and adaptable to the many applications that require reliable semantic parsing. To address the main resource problem involving finding parallel data, we investigate the idea of using source code libraries, or collections of code and text documentation, as a parallel corpus for semantic parser development and introduce 45 new datasets in this domain and a new and challenging text-to-code translation task. As a way of addressing the lack of domain- and language-specific parallel data, we then use these and other benchmark datasets to investigate training se- mantic parsers on multiple datasets, which helps semantic parsers to generalize across different domains and languages and solve new tasks such as polyglot decoding and zero-shot translation (i.e., translating over and between multiple natural and formal languages and unobserved language pairs). Finally, to address the issue of insufficient annotations, we introduce a new learning framework called learning from entailment that uses entailment information (i.e., high-level inferences about whether the meaning of one sentence follows from another) as a weak learning signal to train semantic parsers to reason about the holes in their analysis and learn improved semantic representations. Taken together, this thesis contributes a wide range of new techniques and technical solutions to help build semantic parsing models with minimal amounts of training supervision and manual engineering effort, hence avoiding the resource issues described at the onset. We also introduce a diverse set of new NLU tasks for evaluating semantic parsing models, which we believe help to extend the scope and real world applicability of semantic parsing and computational NLU

Paraphrase identification using knowledge-lean techniques

This research addresses the problem of identification of sentential paraphrases; that is, the ability of an estimator to predict well whether two sentential text fragments are paraphrases. The paraphrase identification task has practical importance in the Natural Language Processing (NLP) community because of the need to deal with the pervasive problem of linguistic variation. Accurate methods for identifying paraphrases should help to improve the performance of NLP systems that require language understanding. This includes key applications such as machine translation, information retrieval and question answering amongst others. Over the course of the last decade, a growing body of research has been conducted on paraphrase identification and it has become an individual working area of NLP. Our objective is to investigate whether techniques concentrating on automated understanding of text requiring less resource may achieve results comparable to methods employing more sophisticated NLP processing tools and other resources. These techniques, which we call “knowledge-lean”, range from simple, shallow overlap methods based on lexical items or n-grams through to more sophisticated methods that employ automatically generated distributional thesauri. The work begins by focusing on techniques that exploit lexical overlap and text-based statistical techniques that are much less in need of NLP tools. We investigate the question “To what extent can these methods be used for the purpose of a paraphrase identification task?” For the two gold standard data, we obtained competitive results on the Microsoft Research Paraphrase Corpus (MSRPC) and reached the state-of-the-art results on the Twitter Paraphrase Corpus, using only n-gram overlap features in conjunction with support vector machines (SVMs). These techniques do not require any language specific tools or external resources and appear to perform well without the need to normalise colloquial language such as that found on Twitter. It was natural to extend the scope of the research and to consider experimenting on another language, which is poor in resources. The scarcity of available paraphrase data led us to construct our own corpus; we have constructed a paraphrasecorpus in Turkish. This corpus is relatively small but provides a representative collection, including a variety of texts. While there is still debate as to whether a binary or fine-grained judgement satisfies a paraphrase corpus, we chose to provide data for a sentential textual similarity task by agreeing on fine-grained scoring, knowing that this could be converted to binary scoring, but not the other way around. The correlation between the results from different corpora is promising. Therefore, it can be surmised that languages poor in resources can benefit from knowledge-lean techniques. Discovering the strengths of knowledge-lean techniques extended with a new perspective to techniques that use distributional statistical features of text by representing each word as a vector (word2vec). While recent research focuses on larger fragments of text with word2vec, such as phrases, sentences and even paragraphs, a new approach is presented by introducing vectors of character n-grams that carry the same attributes as word vectors. The proposed method has the ability to capture syntactic relations as well as semantic relations without semantic knowledge. This is proven to be competitive on Twitter compared to more sophisticated methods

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

Essential Speech and Language Technology for Dutch: Results by the STEVIN-programme

Computational Linguistics; Germanic Languages; Artificial Intelligence (incl. Robotics); Computing Methodologie

Recognizing Paraphrases and Textual Entailment Using Inversion Transduction Grammars

We present first results using paraphrase as well as textual entailment data to test the language universal constraint posited by Wu's (1995, 1997) Inversion Transduction Grammar (ITG) hypothesis. In machine translation and alignment, the ITG Hypothesis provides a strong inductive bias, and has been shown empirically across numerous language pairs and corpora to yield both efficiency and accuracy gains for various language acquisition tasks. Monolingual paraphrase and textual entailment recognition datasets, however, potentially facilitate closer tests of certain aspects of the hypothesis than bilingual parallel corpora, which simultaneously exhibit many irrelevant dimensions of cross-lingual variation. We investigate this using simple generic Bracketing ITGs containing no language-specific linguistic knowledge. Experimental results on the MSR Paraphrase Corpus show that, even in the absence of any thesaurus to accommodate lexical variation between the paraphrases, an uninterpolated average precision of at least 76% is obtainable from the Bracketing ITG's structure matching bias alone. This is consistent with experimental results on the Pascal Recognising Textual Entailment Challenge Corpus, which show surpisingly strong results for a number of the task subsets

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges