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  • Sentence Meaning Representations Across Languages: What CanWe Learn from Existing Frameworks?
    Comput. Linguist. (IF 2.51) Pub Date : 2020-07-16
    Zdeněk Žabokrtský; Daniel Zeman; Magda Ševčíková

    This article gives an overview of how sentence meaning is represented in eleven deep-syntactic frameworks, ranging from those based on linguistic theories elaborated for decades to rather lightweight NLP-motivated approaches. We outline the most important characteristics of each framework and then discuss how particular language phenomena are treated across those frameworks, while trying to shed light

    更新日期:2020-09-03
  • Tractable Lexical-Functional Grammar
    Comput. Linguist. (IF 2.51) Pub Date : 2020-07-16
    Jürgen Wedekind; Ronald M. Kaplan

    The formalism for Lexical-Functional Grammar (LFG) was introduced in the 1980’s as one of the first constraint-based grammatical formalisms for natural language. It has led to substantial contributions to the linguistic literature and to the construction of large-scale descriptions of particular languages. Investigations of its mathematical properties have shown that, without further restrictions,

    更新日期:2020-09-03
  • Predicting In-Game Actions from Interviews of NBA Players
    Comput. Linguist. (IF 2.51) Pub Date : 2020-07-16
    Nadav Oved; Amir Feder; Roi Reichart

    Sports competitions are widely researched in computer and social science, with the goal of understanding how players act under uncertainty. Although there is an abundance of computational work on player metrics prediction based on past performance, very few attempts to incorporate out-of-game signals have been made. Specifically, it was previously unclear whether linguistic signals gathered from players’

    更新日期:2020-08-20
  • Semantic Drift in Multilingual Representations
    Comput. Linguist. (IF 2.51) Pub Date : 2020-07-16
    Lisa Beinborn; Rochelle Choenni

    Multilingual representations have mostly been evaluated based on their performance on specific tasks. In this article, we look beyond engineering goals and analyze the relations between languages in computational representations. We introduce a methodology for comparing languages based on their organization of semantic concepts. We propose to conduct an adapted version of representational similarity

    更新日期:2020-08-20
  • The Limitations of Stylometry for Detecting Machine-Generated Fake News
    Comput. Linguist. (IF 2.51) Pub Date : 2020-07-02
    Tal Schuster; Roei Schuster; Darsh J. Shah; Regina Barzilay

    Recent developments in neural language models (LMs) have raised concerns about their potential misuse for automatically spreading misinformation. In light of these concerns, several studies have proposed to detect machine-generated fake news by capturing their stylistic differences from human-written text. These approaches, broadly termed stylometry, have found success in source attribution and misinformation

    更新日期:2020-08-20
  • Fair Is Better than Sensational: Man Is to Doctor as Woman Is to Doctor
    Comput. Linguist. (IF 2.51) Pub Date : 2020-07-02
    Malvina Nissim; Rik van Noord; Rob van der Goot

    Analogies such as man is to king as woman is to X are often used to illustrate the amazing power of word embeddings. Concurrently, they have also been used to expose how strongly human biases are encoded in vector spaces trained on natural language, with examples like man is to computer programmer as woman is to homemaker. Recent work has shown that analogies are in fact not an accurate diagnostic

    更新日期:2020-08-20
  • Abstract Syntax as Interlingua: Scaling Up the Grammatical Framework from Controlled Languages to Robust Pipelines
    Comput. Linguist. (IF 2.51) Pub Date : 2020-07-02
    Aarne Ranta; Krasimir Angelov; Normunds Gruzitis; Prasanth Kolachina

    Abstract syntax is an interlingual representation used in compilers. Grammatical Framework (GF) applies the abstract syntax idea to natural languages. The development of GF started in 1998, first as a tool for controlled language implementations, where it has gained an established position in both academic and commercial projects. GF provides grammar resources for over 40 languages, enabling accurate

    更新日期:2020-08-20
  • A Systematic Study of Inner-Attention-Based Sentence Representations in Multilingual Neural Machine Translation
    Comput. Linguist. (IF 2.51) Pub Date : 2020-07-02
    Raúl Vázquez; Alessandro Raganato; Mathias Creutz; Jörg Tiedemann

    Neural machine translation has considerably improved the quality of automatic translations by learning good representations of input sentences. In this article, we explore a multilingual translation model capable of producing fixed-size sentence representations by incorporating an intermediate crosslingual shared layer, which we refer to as attention bridge. This layer exploits the semantics from each

    更新日期:2020-08-20
  • LINSPECTOR: Multilingual Probing Tasks for Word Representations
    Comput. Linguist. (IF 2.51) Pub Date : 2020-07-02
    Gözde Gül Şahin; Clara Vania; Ilia Kuznetsov; Iryna Gurevych

    Despite an ever-growing number of word representation models introduced for a large number of languages, there is a lack of a standardized technique to provide insights into what is captured by these models. Such insights would help the community to get an estimate of the downstream task performance, as well as to design more informed neural architectures, while avoiding extensive experimentation that

    更新日期:2020-08-20
  • LessLex: Linking Multilingual Embeddings to SenSe Representations of LEXical Items
    Comput. Linguist. (IF 2.51) Pub Date : 2020-07-02
    Davide Colla; Enrico Mensa; Daniele P. Radicioni

    We present LESSLEX, a novel multilingual lexical resource. Different from the vast majority of existing approaches, we ground our embeddings on a sense inventory made available from the BabelNet semantic network. In this setting, multilingual access is governed by the mapping of terms onto their underlying sense descriptions, such that all vectors co-exist in the same semantic space. As a result, for

    更新日期:2020-08-20
  • Unsupervised Word Translation with Adversarial Autoencoder
    Comput. Linguist. (IF 2.51) Pub Date : 2020-07-02
    Tasnim Mohiuddin; Shafiq Joty

    Crosslingual word embeddings learned from monolingual embeddings have a crucial role in many downstream tasks, ranging from machine translation to transfer learning. Adversarial training has shown impressive success in learning crosslingual embeddings and the associated word translation task without any parallel data by mapping monolingual embeddings to a shared space. However, recent work has shown

    更新日期:2020-08-20
  • Multilingual and Interlingual Semantic Representations for Natural Language Processing: A Brief Introduction
    Comput. Linguist. (IF 2.51) Pub Date : 2020-07-02
    Marta R. Costa-jussà; Cristina España-Bonet; Pascale Fung; Noah A. Smith

    We introduce the Computational Linguistics special issue on Multilingual and Interlingual Semantic Representations for Natural Language Processing. We situate the special issue’s five articles in the context of our fast-changing field, explaining our motivation for this project. We offer a brief summary of the work in the issue, which includes developments on lexical and sentential semantic representations

    更新日期:2020-08-20
  • Fruit Carts: A Domain and Corpus for Research in Dialogue Systems and Psycholinguistics.
    Comput. Linguist. (IF 2.51) Pub Date : 2012-09-01
    Gregory Aist,Ellen Campana,James Allen,Mary Swift,Michael K Tanenhaus

    We describe a novel domain, Fruit Carts, aimed at eliciting human language production for the twin purposes of (a) dialogue system research and development and (b) psycholinguistic research. Fruit Carts contains five tasks: choosing a cart, placing it on a map, painting the cart, rotating the cart, and filling the cart with fruit. Fruit Carts has been used for research in psycholinguistics and in dialogue

    更新日期:2019-11-01
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