ymoslem/acl-abstracts huggingface dataset:
ODIL\_Syntax: a Free Spontaneous Spoken French Treebank Annotated with Constituent Trees (2020)

This paper describes ODIL Syntax, a French treebank built on spontaneous speech transcripts. The syntactic structure of every speech turn is represented by constituent trees, through a procedure which combines an automatic annotation provided by a parser (here, the Stanford Parser) and a manual revision. ODIL Syntax respects the annotation scheme designed for the French TreeBank (FTB), with the addition of some annotation guidelines that aims at representing specific features of the spoken language such as speech disfluencies. The corpus will be freely distributed by January 2020 under a Creative Commons licence. It will ground a further semantic enrichment dedicated to the representation of temporal entities and temporal relations, as a second phase of the ODIL@Temporal project. The paper details the annotation scheme we followed with a emphasis on the representation of speech disfluencies. We then present the annotation procedure that was carried out on the Contemplata annotation platform. In the last section, we provide some distributional characteristics of the annotated corpus (POS distribution, multiword expressions).
Towards the Conversion of National Corpus of Polish to Universal Dependencies (2020)

The research presented in this paper aims at enriching the manually morphosyntactically annotated part of National Corpus of Polish (NKJP1M) with a syntactic layer, i.e. dependency trees of sentences, and at converting both dependency trees and morphosyntactic annotations of particular tokens to Universal Dependencies. The dependency layer is built using a semi-automatic annotation procedure. The sentences from NKJP1M are first parsed with a dependency parser trained on Polish Dependency Bank, i.e. the largest bank of Polish dependency trees. The predicted dependency trees and the morphosyntactic annotations of tokens are then automatically converted into UD dependency graphs. NKJP1M sentences are an essential part of Polish Dependency Bank, we thus replace some automatically predicted dependency trees with their manually annotated equivalents. The final dependency treebank consists of 86K trees (including 15K gold-standard trees). A natural language pre-processing model trained on the enlarged set of (possibly noisy) dependency trees outperforms a model trained on a smaller set of the gold-standard trees in predicting part-of-speech tags, morphological features, lemmata, and labelled dependency trees
SegBo: A Database of Borrowed Sounds in the World`s Languages (2020)

Phonological segment borrowing is a process through which languages acquire new contrastive speech sounds as the result of borrowing new words from other languages. Despite the fact that phonological segment borrowing is documented in many of the world`s languages, to date there has been no large-scale quantitative study of the phenomenon. In this paper, we present SegBo, a novel cross-linguistic database of borrowed phonological segments. We describe our data aggregation pipeline and the resulting language sample. We also present two short case studies based on the database. The first deals with the impact of large colonial languages on the sound systems of the world`s languages; the second deals with universals of borrowing in the domain of rhotic consonants.
Developing Resources for Automated Speech Processing of Quebec French (2020)

The analysis of the structure of speech nearly always rests on the alignment of the speech recording with a phonetic transcription. Nowadays several tools can perform this speech segmentation automatically. However, none of them allows the automatic segmentation of Quebec French (QF hereafter), the acoustics and phonotactics of QF differing widely from that of France French (FF hereafter). To adequately segment QF, features like diphthongization of long vowels and affrication of coronal stops have to be taken into account. Thus acoustic models for automatic segmentation must be trained on speech samples exhibiting those phenomena. Dictionaries and lexicons must also be adapted and integrate differences in lexical units and in the phonology of QF. This paper presents the development of linguistic resources to be included into SPPAS software tool in order to get Text normalization, Phonetization, Alignment and Syllabification. We adapted the existing French lexicon and developed a QF-specific pronunciation dictionary. We then created an acoustic model from the existing ones and adapted it with 5 minutes of manually time-aligned data. These new resources are all freely distributed with SPPAS version 2.7; they perform the full process of speech segmentation in Quebec French.
AlloVera: A Multilingual Allophone Database (2020)

We introduce a new resource, AlloVera, which provides mappings from 218 allophones to phonemes for 14 languages. Phonemes are contrastive phonological units, and allophones are their various concrete realizations, which are predictable from phonological context. While phonemic representations are language specific, phonetic representations (stated in terms of (allo)phones) are much closer to a universal (language-independent) transcription. AlloVera allows the training of speech recognition models that output phonetic transcriptions in the International Phonetic Alphabet (IPA), regardless of the input language. We show that a {\textquotedblleft}universal{\textquotedblright} allophone model, Allosaurus, built with AlloVera, outperforms {\textquotedblleft}universal{\textquotedblright} phonemic models and language-specific models on a speech-transcription task. We explore the implications of this technology (and related technologies) for the documentation of endangered and minority languages. We further explore other applications for which AlloVera will be suitable as it grows, including phonological typology.
Arabic Speech Rhythm Corpus: Read and Spontaneous Speaking Styles (2020)

Databases for studying speech rhythm and tempo exist for numerous languages. The present corpus was built to allow comparisons between Arabic speech rhythm and other languages. 10 Egyptian speakers (gender-balanced) produced speech in two different speaking styles (read and spontaneous). The design of the reading task replicates the methodology used in the creation of BonnTempo corpus (BTC). During the spontaneous task, speakers talked freely for more than one minute about their daily life and/or their studies, then they described the directions to come to the university from a famous near location using a map as a visual stimulus. For corpus annotation, the database has been manually and automatically time-labeled, which makes it feasible to perform a quantitative analysis of the rhythm of Arabic in both Modern Standard Arabic (MSA) and Egyptian dialect variety. The database serves as a phonetic resource, which allows researchers to examine various aspects of Arabic supra-segmental features and it can be used for forensic phonetic research, for comparison of different speakers, analyzing variability in different speaking styles, and automatic speech and speaker recognition.
Comparing Methods for Measuring Dialect Similarity in Norwegian (2020)

The present article presents four experiments with two different methods for measuring dialect similarity in Norwegian: the Levenshtein method and the neural long short term memory (LSTM) autoencoder network, a machine learning algorithm. The visual output in the form of dialect maps is then compared with canonical maps found in the dialect literature. All of this enables us to say that one does not need fine-grained transcriptions of speech to replicate classical classification patterns.
AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition (2020)

Modern Automatic Speech Recognition (ASR) technology has evolved to identify the speech spoken by native speakers of a language very well. However, identification of the speech spoken by non-native speakers continues to be a major challenge for it. In this work, we first spell out the key requirements for creating a well-curated database of speech samples in non-native accents for training and testing robust ASR systems. We then introduce AccentDB, one such database that contains samples of 4 Indian-English accents collected by us, and a compilation of samples from 4 native-English, and a metropolitan Indian-English accent. We also present an analysis on separability of the collected accent data. Further, we present several accent classification models and evaluate them thoroughly against human-labelled accent classes. We test the generalization of our classifier models in a variety of setups of seen and unseen data. Finally, we introduce accent neutralization of non-native accents to native accents using autoencoder models with task-specific architectures. Thus, our work aims to aid ASR systems at every stage of development with a database for training, classification models for feature augmentation, and neutralization systems for acoustic transformations of non-native accents of English.
A Framework for Evaluation of Machine Reading Comprehension Gold Standards (2020)

Machine Reading Comprehension (MRC) is the task of answering a question over a paragraph of text. While neural MRC systems gain popularity and achieve noticeable performance, issues are being raised with the methodology used to establish their performance, particularly concerning the data design of gold standards that are used to evaluate them. There is but a limited understanding of the challenges present in this data, which makes it hard to draw comparisons and formulate reliable hypotheses. As a first step towards alleviating the problem, this paper proposes a unifying framework to systematically investigate the present linguistic features, required reasoning and background knowledge and factual correctness on one hand, and the presence of lexical cues as a lower bound for the requirement of understanding on the other hand. We propose a qualitative annotation schema for the first and a set of approximative metrics for the latter. In a first application of the framework, we analyse modern MRC gold standards and present our findings: the absence of features that contribute towards lexical ambiguity, the varying factual correctness of the expected answers and the presence of lexical cues, all of which potentially lower the reading comprehension complexity and quality of the evaluation data.
Multi-class Hierarchical Question Classification for Multiple Choice Science Exams (2020)

Prior work has demonstrated that question classification (QC), recognizing the problem domain of a question, can help answer it more accurately. However, developing strong QC algorithms has been hindered by the limited size and complexity of annotated data available. To address this, we present the largest challenge dataset for QC, containing 7,787 science exam questions paired with detailed classification labels from a fine-grained hierarchical taxonomy of 406 problem domains. We then show that a BERT-based model trained on this dataset achieves a large (+0.12 MAP) gain compared with previous methods, while also achieving state-of-the-art performance on benchmark open-domain and biomedical QC datasets. Finally, we show that using this model`s predictions of question topic significantly improves the accuracy of a question answering system by +1.7{\%} P@1, with substantial future gains possible as QC performance improves.
Assessing Users' Reputation from Syntactic and Semantic Information in Community Question Answering (2020)

Textual content is the most significant as well as substantially the big part of CQA (Community Question Answering) forums. Users gain reputation for contributing such content. Although linguistic quality is the very essence of textual information, that does not seem to be considered in estimating users' reputation. As existing users' reputation systems seem to solely rely on vote counting, adding that bit of linguistic information surely improves their quality. In this study, we investigate the relationship between users' reputation and linguistic features extracted from their associated answers content. And we build statistical models on a Stack Overflow dataset that learn reputation from complex syntactic and semantic structures of such content. The resulting models reveal how users' writing styles in answering questions play important roles in building reputation points. In our experiments, extracting answers from systematically selected users followed by linguistic features annotation and models building. The models are evaluated on in-domain (e.g., Server Fault, Super User) and out-domain (e.g., English, Maths) datasets. We found out that the selected linguistic features have quite significant influences over reputation scores. In the best case scenario, the selected linguistic feature set could explain 80{\%} variation in reputation scores with the prediction error of 3{\%}. The performance results obtained from the baseline models have been significantly improved by adding syntactic and punctuation marks features.
Unsupervised Domain Adaptation of Language Models for Reading Comprehension (2020)

This study tackles unsupervised domain adaptation of reading comprehension (UDARC). Reading comprehension (RC) is a task to learn the capability for question answering with textual sources. State-of-the-art models on RC still do not have general linguistic intelligence; i.e., their accuracy worsens for out-domain datasets that are not used in the training. We hypothesize that this discrepancy is caused by a lack of the language modeling (LM) capability for the out-domain. The UDARC task allows models to use supervised RC training data in the source domain and only unlabeled passages in the target domain. To solve the UDARC problem, we provide two domain adaptation models. The first one learns the out-domain LM and in-domain RC task sequentially. The second one is the proposed model that uses a multi-task learning approach of LM and RC. The models can retain both the RC capability acquired from the supervised data in the source domain and the LM capability from the unlabeled data in the target domain. We evaluated the models on UDARC with five datasets in different domains. The models outperformed the model without domain adaptation. In particular, the proposed model yielded an improvement of 4.3/4.2 points in EM/F1 in an unseen biomedical domain.
Propagate-Selector: Detecting Supporting Sentences for Question Answering via Graph Neural Networks (2020)

In this study, we propose a novel graph neural network called propagate-selector (PS), which propagates information over sentences to understand information that cannot be inferred when considering sentences in isolation. First, we design a graph structure in which each node represents an individual sentence, and some pairs of nodes are selectively connected based on the text structure. Then, we develop an iterative attentive aggregation and a skip-combine method in which a node interacts with its neighborhood nodes to accumulate the necessary information. To evaluate the performance of the proposed approaches, we conduct experiments with the standard HotpotQA dataset. The empirical results demonstrate the superiority of our proposed approach, which obtains the best performances, compared to the widely used answer-selection models that do not consider the intersentential relationship.
An Empirical Comparison of Question Classification Methods for Question Answering Systems (2020)

Question classification is an important component of Question Answering Systems responsible for identifying the type of an answer a particular question requires. For instance, {\textquotedblleft}Who is the prime minister of the United Kingdom?{\textquotedblright} demands a name of a PERSON, while {\textquotedblleft}When was the queen of the United Kingdom born?{\textquotedblright} entails a DATE. This work makes an extensible review of the most recent methods for Question Classification, taking into consideration their applicability in low-resourced languages. First, we propose a manual classification of the current state-of-the-art methods in four distinct categories: low, medium, high, and very high level of dependency on external resources. Second, we applied this categorization in an empirical comparison in terms of the amount of data necessary for training and performance in different languages. In addition to complementing earlier works in this field, our study shows a boost on methods relying on recent language models, overcoming methods not suitable for low-resourced languages.
Cross-sentence Pre-trained Model for Interactive QA matching (2020)

Semantic matching measures the dependencies between query and answer representations, it is an important criterion for evaluating whether the matching is successful. In fact, such matching does not examine each sentence individually, context information outside a sentence should be considered equally important to the syntactic context inside a sentence. We proposed a new QA matching model, built upon a cross-sentence context-aware architecture. An interactive attention mechanism with a pre-trained language model is proposed to automatically select salient positional answer representations that contribute more significantly to the answer relevance of a given question. In addition to the context information captured at each word position, we incorporate a new quantity of context information jump to facilitate the attention weight formulation. This reflects the amount of new information brought by the next word and is computed by modeling the joint probability between two adjacent word states. The proposed method is compared to multiple state-of-the-art ones evaluated using the TREC library, WikiQA, and the Yahoo! community question datasets. Experimental results show that the proposed method outperforms satisfactorily the competing ones.
SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.0 (2020)

Existing machine reading comprehension models are reported to be brittle for adversarially perturbed questions when optimizing only for accuracy, which led to the creation of new reading comprehension benchmarks, such as SQuAD 2.0 which contains such type of questions. However, despite the super-human accuracy of existing models on such datasets, it is still unclear how the model predicts the answerability of the question, potentially due to the absence of a shared annotation for the explanation. To address such absence, we release SQuAD2-CR dataset, which contains annotations on unanswerable questions from the SQuAD 2.0 dataset, to enable an explanatory analysis of the model prediction. Specifically, we annotate (1) explanation on why the most plausible answer span cannot be the answer and (2) which part of the question causes unanswerability. We share intuitions and experimental results that how this dataset can be used to analyze and improve the interpretability of existing reading comprehension model behavior.
Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs (2020)

This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data. Using the framework of role play-based question answering, we collected single-turn question-answer pairs for particular characters from online users. Meta information was also collected such as emotion and intimacy related to question-answer pairs. We verified the quality of the collected data and, by subjective evaluation, we also verified their usefulness in training neural conversational models for generating utterances reflecting the meta information, especially emotion.
AIA-BDE: A Corpus of FAQs in Portuguese and their Variations (2020)

We present AIA-BDE, a corpus of 380 domain-oriented FAQs in Portuguese and their variations, i.e., paraphrases or entailed questions, created manually, by humans, or automatically, with Google Translate. Its aims to be used as a benchmark for FAQ retrieval and automatic question-answering, but may be useful in other contexts, such as the development of task-oriented dialogue systems, or models for natural language inference in an interrogative context. We also report on two experiments. Matching variations with their original questions was not trivial with a set of unsupervised baselines, especially for manually created variations. Besides high performances obtained with ELMo and BERT embeddings, an Information Retrieval system was surprisingly competitive when considering only the first hit. In the second experiment, text classifiers were trained with the original questions, and tested when assigning each variation to one of three possible sources, or assigning them as out-of-domain. Here, the difference between manual and automatic variations was not so significant.
TutorialVQA: Question Answering Dataset for Tutorial Videos (2020)

Despite the number of currently available datasets on video-question answering, there still remains a need for a dataset involving multi-step and non-factoid answers. Moreover, relying on video transcripts remains an under-explored topic. To adequately address this, we propose a new question answering task on instructional videos, because of their verbose and narrative nature. While previous studies on video question answering have focused on generating a short text as an answer, given a question and video clip, our task aims to identify a span of a video segment as an answer which contains instructional details with various granularities. This work focuses on screencast tutorial videos pertaining to an image editing program. We introduce a dataset, TutorialVQA, consisting of about 6,000 manually collected triples of (video, question, answer span). We also provide experimental results with several baseline algorithms using the video transcripts. The results indicate that the task is challenging and call for the investigation of new algorithms.
WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference (2020)

Explainable question answering for complex questions often requires combining large numbers of facts to answer a question while providing a human-readable explanation for the answer, a process known as multi-hop inference. Standardized science questions require combining an average of 6 facts, and as many as 16 facts, in order to answer and explain, but most existing datasets for multi-hop reasoning focus on combining only two facts, significantly limiting the ability of multi-hop inference algorithms to learn to generate large inferences. In this work we present the second iteration of the WorldTree project, a corpus of 5,114 standardized science exam questions paired with large detailed multi-fact explanations that combine core scientific knowledge and world knowledge. Each explanation is represented as a lexically-connected {\textquotedblleft}explanation graph{\textquotedblright} that combines an average of 6 facts drawn from a semi-structured knowledge base of 9,216 facts across 66 tables. We use this explanation corpus to author a set of 344 high-level science domain inference patterns similar to semantic frames supporting multi-hop inference. Together, these resources provide training data and instrumentation for developing many-fact multi-hop inference models for question answering.