ymoslem/acl-abstracts huggingface dataset:
French Coreference for Spoken and Written Language (2020)

Coreference resolution aims at identifying and grouping all mentions referring to the same entity. In French, most systems run different setups, making their comparison difficult. In this paper, we present an extensive comparison of several coreference resolution systems for French. The systems have been trained on two corpora (ANCOR for spoken language and Democrat for written language) annotated with coreference chains, and augmented with syntactic and semantic information. The models are compared with different configurations (e.g. with and without singletons). In addition, we evaluate mention detection and coreference resolution apart. We present a full-stack model that outperforms other approaches. This model allows us to study the impact of mention detection errors on coreference resolution. Our analysis shows that mention detection can be improved by focusing on boundary identification while advances in the pronoun-noun relation detection can help the coreference task. Another contribution of this work is the first end-to-end neural French coreference resolution model trained on Democrat (written texts), which compares to the state-of-the-art systems for oral French.
Cross-lingual Zero Pronoun Resolution (2020)

In languages like Arabic, Chinese, Italian, Japanese, Korean, Portuguese, Spanish, and many others, predicate arguments in certain syntactic positions are not realized instead of being realized as overt pronouns, and are thus called zero- or null-pronouns. Identifying and resolving such omitted arguments is crucial to machine translation, information extraction and other NLP tasks, but depends heavily on semantic coherence and lexical relationships. We propose a BERT-based cross-lingual model for zero pronoun resolution, and evaluate it on the Arabic and Chinese portions of OntoNotes 5.0. As far as we know, ours is the first neural model of zero-pronoun resolution for Arabic; and our model also outperforms the state-of-the-art for Chinese. In the paper we also evaluate BERT feature extraction and fine-tune models on the task, and compare them with our model. We also report on an investigation of BERT layers indicating which layer encodes the most suitable representation for the task.
Exploiting Cross-Lingual Hints to Discover Event Pronouns (2020)

Non-nominal co-reference is much less studied than nominal coreference, partly because of the lack of annotated corpora. We explore the possibility to exploit parallel multilingual corpora as a means of cheap supervision for the classification of three different readings of the English pronoun {\textquoteleft}it': entity, event or pleonastic, from their translation in several languages. We found that the {\textquoteleft}event' reading is not very frequent, but can be easily predicted provided that the construction used to translate the {\textquoteleft}it' example is a pronoun as well. These cases, nevertheless, are not enough to generalize to other types of non-nominal reference.
MuDoCo: Corpus for Multidomain Coreference Resolution and Referring Expression Generation (2020)

This paper proposes a new dataset, MuDoCo, composed of authored dialogs between a fictional user and a system who are given tasks to perform within six task domains. These dialogs are given rich linguistic annotations by expert linguists for several types of reference mentions and named entity mentions, either of which can span multiple words, as well as for coreference links between mentions. The dialogs sometimes cross and blend domains, and the users exhibit complex task switching behavior such as re-initiating a previous task in the dialog by referencing the entities within it. The dataset contains a total of 8,429 dialogs with an average of 5.36 turns per dialog. We are releasing this dataset to encourage research in the field of coreference resolution, referring expression generation and identification within realistic, deep dialogs involving multiple domains. To demonstrate its utility, we also propose two baseline models for the downstream tasks: coreference resolution and referring expression generation.
Affection Driven Neural Networks for Sentiment Analysis (2020)

Deep neural network models have played a critical role in sentiment analysis with promising results in the recent decade. One of the essential challenges, however, is how external sentiment knowledge can be effectively utilized. In this work, we propose a novel affection-driven approach to incorporating affective knowledge into neural network models. The affective knowledge is obtained in the form of a lexicon under the Affect Control Theory (ACT), which is represented by vectors of three-dimensional attributes in Evaluation, Potency, and Activity (EPA). The EPA vectors are mapped to an affective influence value and then integrated into Long Short-term Memory (LSTM) models to highlight affective terms. Experimental results show a consistent improvement of our approach over conventional LSTM models by 1.0{\%} to 1.5{\%} in accuracy on three large benchmark datasets. Evaluations across a variety of algorithms have also proven the effectiveness of leveraging affective terms for deep model enhancement.
The Alice Datasets: fMRI \& EEG Observations of Natural Language Comprehension (2020)

The Alice Datasets are a set of datasets based on magnetic resonance data and electrophysiological data, collected while participants heard a story in English. Along with the datasets and the text of the story, we provide a variety of different linguistic and computational measures ranging from prosodic predictors to predictors capturing hierarchical syntactic information. These ecologically valid datasets can be easily reused to replicate prior work and to test new hypotheses about natural language comprehension in the brain.
Modelling Narrative Elements in a Short Story: A Study on Annotation Schemes and Guidelines (2020)

Text-processing algorithms that annotate main components of a story-line are presently in great need of corpora and well-agreed annotation schemes. The Text World Theory of cognitive linguistics offers a model that generalizes a narrative structure in the form of world building elements (characters, time and space) as well as text worlds themselves and switches between them. We have conducted a survey on how text worlds and their elements are annotated in different projects and proposed our own annotation scheme and instructions. We tested them, first, on the science fiction story {\textquotedblleft}We Can Remember It for You Wholesale{\textquotedblright} by Philip K. Dick. Then we corrected the guidelines and added computer annotation of verb forms with the purpose to get a higher raters' agreement and tested them again on the short story {\textquotedblleft}The Gift of the Magi{\textquotedblright} by O. Henry. As a result, the agreement among the three raters has risen. With due revision and tests, our annotation scheme and guidelines can be used for annotating narratives in corpora of literary texts, criminal evidence, teaching materials, quests, etc.
Cortical Speech Databases For Deciphering the Articulatory Code (2020)

The paper relates to following {\textquoteleft}AC-hypotheses': The articulatory code (AC) is a neural code exchanging multi-item messages between the short-term memory and cortical areas as the vSMC and STG. In these areas already neurons active in the presence of articulatory features have been measured. The AC codes the content of speech segmented in chunks and is the same for both modalities - speech perception and speech production. Each AC-message is related to a syllable. The items of each message relate to coordinated articulatory gestures composing the syllable. The mechanism to transport the AC and to segment the auditory signal is based on Ɵ/{\ensuremath{\gamma}}-oscillations, where a Ɵ-cycle has the duration of a Ɵ-syllable. The paper describes the findings from neuroscience, phonetics and the science of evolution leading to the AC-hypotheses. The paper proposes to verify the AC-hypotheses by measuring the activity of all ensembles of neurons coding and decoding the AC. Due to state of the art, the cortical measurements to be prepared, done and further processed need a high effort from scientists active in different areas. We propose to launch a project to produce cortical speech databases with cortical recordings synchronized with the speech signal allowing to decipher the articulatory code.
ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation (2020)

We recorded and preprocessed ZuCo 2.0, a new dataset of simultaneous eye-tracking and electroencephalography during natural reading and during annotation. This corpus contains gaze and brain activity data of 739 English sentences, 349 in a normal reading paradigm and 390 in a task-specific paradigm, in which the 18 participants actively search for a semantic relation type in the given sentences as a linguistic annotation task. This new dataset complements ZuCo 1.0 by providing experiments designed to analyze the differences in cognitive processing between natural reading and annotation. The data is freely available here: \url{https://osf.io/2urht/}.
Linguistic, Kinematic and Gaze Information in Task Descriptions: The LKG-Corpus (2020)

Data from neuroscience and psychology suggest that sensorimotor cognition may be of central importance to language. Specifically, the linguistic structure of utterances referring to concrete actions may reflect the structure of the sensorimotor processing underlying the same action. To investigate this, we present the Linguistic, Kinematic and Gaze information in task descriptions Corpus (LKG-Corpus), comprising multimodal data on 13 humans, conducting take, put, and push actions, and describing these actions with 350 utterances. Recorded are audio, video, motion and eye-tracking data while participants perform an action and describe what they do. The dataset is annotated with orthographic transcriptions of utterances and information on: (a) gaze behaviours, (b) when a participant touched an object, (c) when an object was moved, (d) when a participant looked at the location s/he would next move the object to, (e) when the participant`s gaze was stable on an area. With the exception of the annotation of stable gaze, all annotations were performed manually. With the LKG-Corpus, we present a dataset that integrates linguistic, kinematic and gaze data with an explicit focus on relations between action and language. On this basis, we outline applications of the dataset to both basic and applied research.
The ACQDIV Corpus Database and Aggregation Pipeline (2020)

We present the ACQDIV corpus database and aggregation pipeline, a tool developed as part of the European Research Council (ERC) funded project ACQDIV, which aims to identify the universal cognitive processes that allow children to acquire any language. The corpus database represents 15 corpora from 14 typologically maximally diverse languages. Here we give an overview of the project, database, and our extensible software package for adding more corpora to the current language sample. Lastly, we discuss how we use the corpus database to mine for universal patterns in child language acquisition corpora and we describe avenues for future research.
Providing Semantic Knowledge to a Set of Pictograms for People with Disabilities: a Set of Links between WordNet and Arasaac: Arasaac-WN (2020)

This article presents a resource that links WordNet, the widely known lexical and semantic database, and Arasaac, the largest freely available database of pictograms. Pictograms are a tool that is more and more used by people with cognitive or communication disabilities. However, they are mainly used manually via workbooks, whereas caregivers and families would like to use more automated tools (use speech to generate pictograms, for example). In order to make it possible to use pictograms automatically in NLP applications, we propose a database that links them to semantic knowledge. This resource is particularly interesting for the creation of applications that help people with cognitive disabilities, such as text-to-picto, speech-to-picto, picto-to-speech... In this article, we explain the needs for this database and the problems that have been identified. Currently, this resource combines approximately 800 pictograms with their corresponding WordNet synsets and it is accessible both through a digital collection and via an SQL database. Finally, we propose a method with associated tools to make our resource language-independent: this method was applied to create a first text-to-picto prototype for the French language. Our resource is distributed freely under a Creative Commons license at the following URL: \url{https://github.com/getalp/Arasaac-WN}.
Orthographic Codes and the Neighborhood Effect: Lessons from Information Theory (2020)

We consider the orthographic neighborhood effect: the effect that words with more orthographic similarity to other words are read faster. The neighborhood effect serves as an important control variable in psycholinguistic studies of word reading, and explains variance in addition to word length and word frequency. Following previous work, we model the neighborhood effect as the average distance to neighbors in feature space for three feature sets: slots, character ngrams and skipgrams. We optimize each of these feature sets and find evidence for language-independent optima, across five megastudy corpora from five alphabetic languages. Additionally, we show that weighting features using the inverse of mutual information (MI) improves the neighborhood effect significantly for all languages. We analyze the inverse feature weighting, and show that, across languages, grammatical morphemes get the lowest weights. Finally, we perform the same experiments on Korean Hangul, a non-alphabetic writing system, where we find the opposite results: slower responses as a function of denser neighborhoods, and a negative effect of inverse feature weighting. This raises the question of whether this is a cognitive effect, or an effect of the way we represent Hangul orthography, and indicates more research is needed.
Understanding the Dynamics of Second Language Writing through Keystroke Logging and Complexity Contours (2020)

The purpose of this paper is twofold: [1] to introduce, to our knowledge, the largest available resource of keystroke logging (KSL) data generated by Etherpad (\url{https://etherpad.org/}), an open-source, web-based collaborative real-time editor, that captures the dynamics of second language (L2) production and [2] to relate the behavioral data from KSL to indices of syntactic and lexical complexity of the texts produced obtained from a tool that implements a sliding window approach capturing the progression of complexity within a text. We present the procedures and measures developed to analyze a sample of 14,913,009 keystrokes in 3,454 texts produced by 512 university students (upper-intermediate to advanced L2 learners of English) (95,354 sentences and 18,32,027 words) aiming to achieve a better alignment between keystroke-logging measures and underlying cognitive processes, on the one hand, and L2 writing performance measures, on the other hand. The resource introduced in this paper is a reflection of increasing recognition of the urgent need to obtain ecologically valid data that have the potential to transform our current understanding of mechanisms underlying the development of literacy (reading and writing) skills.
Design of BCCWJ-EEG: Balanced Corpus with Human Electroencephalography (2020)

The past decade has witnessed the happy marriage between natural language processing (NLP) and the cognitive science of language. Moreover, given the historical relationship between biological and artificial neural networks, the advent of deep learning has re-sparked strong interests in the fusion of NLP and the neuroscience of language. Importantly, this inter-fertilization between NLP, on one hand, and the cognitive (neuro)science of language, on the other, has been driven by the language resources annotated with human language processing data. However, there remain several limitations with those language resources on annotations, genres, languages, etc. In this paper, we describe the design of a novel language resource called BCCWJ-EEG, the Balanced Corpus of Contemporary Written Japanese (BCCWJ) experimentally annotated with human electroencephalography (EEG). Specifically, after extensively reviewing the language resources currently available in the literature with special focus on eye-tracking and EEG, we summarize the details concerning (i) participants, (ii) stimuli, (iii) procedure, (iv) data preprocessing, (v) corpus evaluation, (vi) resource release, and (vii) compilation schedule. In addition, potential applications of BCCWJ-EEG to neuroscience and NLP will also be discussed.
Using the RUPEX Multichannel Corpus in a Pilot fMRI Study on Speech Disfluencies (2020)

In modern linguistics and psycholinguistics speech disfluencies in real fluent speech are a well-known phenomenon. But it`s not still clear which components of brain systems are involved into its comprehension in a listener`s brain. In this paper we provide a pilot neuroimaging study of the possible neural correlates of speech disfluencies perception, using a combination of the corpus and functional magnetic-resonance imaging (fMRI) methods. Special technical procedure of selecting stimulus material from Russian multichannel corpus RUPEX allowed to create fragments in terms of requirements for the fMRI BOLD temporal resolution. They contain isolated speech disfluencies and their clusters. Also, we used the referential task for participants fMRI scanning. As a result, it was demonstrated that annotated multichannel corpora like RUPEX can be an important resource for experimental research in interdisciplinary fields. Thus, different aspects of communication can be explored through the prism of brain activation.
Construction of an Evaluation Corpus for Grammatical Error Correction for Learners of Japanese as a Second Language (2020)

The NAIST Lang-8 Learner Corpora (Lang-8 corpus) is one of the largest second-language learner corpora. The Lang-8 corpus is suitable as a training dataset for machine translation-based grammatical error correction systems. However, it is not suitable as an evaluation dataset because the corrected sentences sometimes include inappropriate sentences. Therefore, we created and released an evaluation corpus for correcting grammatical errors made by learners of Japanese as a Second Language (JSL). As our corpus has less noise and its annotation scheme reflects the characteristics of the dataset, it is ideal as an evaluation corpus for correcting grammatical errors in sentences written by JSL learners. In addition, we applied neural machine translation (NMT) and statistical machine translation (SMT) techniques to correct the grammar of the JSL learners' sentences and evaluated their results using our corpus. We also compared the performance of the NMT system with that of the SMT system.
Effective Crowdsourcing of Multiple Tasks for Comprehensive Knowledge Extraction (2020)

Information extraction from unstructured texts plays a vital role in the field of natural language processing. Although there has been extensive research into each information extraction task (i.e., entity linking, coreference resolution, and relation extraction), data are not available for a continuous and coherent evaluation of all information extraction tasks in a comprehensive framework. Given that each task is performed and evaluated with a different dataset, analyzing the effect of the previous task on the next task with a single dataset throughout the information extraction process is impossible. This paper aims to propose a Korean information extraction initiative point and promote research in this field by presenting crowdsourcing data collected for four information extraction tasks from the same corpus and the training and evaluation results for each task of a state-of-the-art model. These machine learning data for Korean information extraction are the first of their kind, and there are plans to continuously increase the data volume. The test results will serve as an initiative result for each Korean information extraction task and are expected to serve as a comparison target for various studies on Korean information extraction using the data collected in this study.
Developing a Corpus of Indirect Speech Act Schemas (2020)

Resolving Indirect Speech Acts (ISAs), in which the intended meaning of an utterance is not identical to its literal meaning, is essential to enabling the participation of intelligent systems in peoples' everyday lives. Especially challenging are those cases in which the interpretation of such ISAs depends on context. To test a system`s ability to perform ISA resolution we need a corpus, but developing such a corpus is difficult, especialy given the contex-dependent requirement. This paper addresses the difficult problems of constructing a corpus of ISAs, taking inspiration from relevant work in using corpora for reasoning tasks. We present a formal representation of ISA Schemas required for such testing, including a measure of the difficulty of a particular schema. We develop an approach to authoring these schemas using corpus analysis and crowdsourcing, to maximize realism and minimize the amount of expert authoring needed. Finally, we describe several characteristics of collected data, and potential future work.
Quality Estimation for Partially Subjective Classification Tasks via Crowdsourcing (2020)

The quality estimation of artifacts generated by creators via crowdsourcing has great significance for the construction of a large-scale data resource. A common approach to this problem is to ask multiple reviewers to evaluate the same artifacts. However, the commonly used majority voting method to aggregate reviewers' evaluations does not work effectively for partially subjective or purely subjective tasks because reviewers' sensitivity and bias of evaluation tend to have a wide variety. To overcome this difficulty, we propose a probabilistic model for subjective classification tasks that incorporates the qualities of artifacts as well as the abilities and biases of creators and reviewers as latent variables to be jointly inferred. We applied this method to the partially subjective task of speech classification into the following four attitudes: agreement, disagreement, stalling, and question. The result shows that the proposed method estimates the quality of speech more effectively than a vote aggregation, measured by correlation with a fine-grained classification by experts.
Crowdsourcing in the Development of a Multilingual FrameNet: A Case Study of Korean FrameNet (2020)

Using current methods, the construction of multilingual resources in FrameNet is an expensive and complex task. While crowdsourcing is a viable alternative, it is difficult to include non-native English speakers in such efforts as they often have difficulty with English-based FrameNet tools. In this work, we investigated cross-lingual issues in crowdsourcing approaches for multilingual FrameNets, specifically in the context of the newly constructed Korean FrameNet. To accomplish this, we evaluated the effectiveness of various crowdsourcing settings whereby certain types of information are provided to workers, such as English definitions in FrameNet or translated definitions. We then evaluated whether the crowdsourced results accurately captured the meaning of frames both cross-culturally and cross-linguistically, and found that by allowing the crowd workers to make intuitive choices, they achieved a quality comparable to that of trained FrameNet experts (F1 {\ensuremath{>}} 0.75). The outcomes of this work are now publicly available as a new release of Korean FrameNet 1.1.