Venelin Kovatchev


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InferES : A Natural Language Inference Corpus for Spanish Featuring Negation-Based Contrastive and Adversarial Examples
Venelin Kovatchev | Mariona Taulé
Proceedings of the 29th International Conference on Computational Linguistics

In this paper we present InferES - an original corpus for Natural Language Inference (NLI) in European Spanish. We propose, implement, and analyze a variety of corpus-creating strategies utilizing expert linguists and crowd workers. The objectives behind InferES are to provide high-quality data, and at the same time to facilitate the systematic evaluation of automated systems. Specifically, we focus on measuring and improving the performance of machine learning systems on negation-based adversarial examples and their ability to generalize across out-of-distribution topics. We train two transformer models on InferES (8,055 gold examples) in a variety of scenarios. Our best model obtains 72.8% accuracy, leaving a lot of room for improvement. The “hypothesis-only” baseline performs only 2%-5% higher than majority, indicating much fewer annotation artifacts than prior work. We show that models trained on InferES generalize very well across topics (both in- and out-of-distribution) and perform moderately well on negation-based adversarial examples.

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longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.
Venelin Kovatchev | Trina Chatterjee | Venkata S Govindarajan | Jifan Chen | Eunsol Choi | Gabriella Chronis | Anubrata Das | Katrin Erk | Matthew Lease | Junyi Jessy Li | Yating Wu | Kyle Mahowald
Proceedings of the First Workshop on Dynamic Adversarial Data Collection

Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability. Here, we describe the approach of the team “longhorns” on Task 1 of the The First Workshop on Dynamic Adversarial Data Collection (DADC), which asked teams to manually fool a model on an Extractive Question Answering task. Our team finished first (pending validation), with a model error rate of 62%. We advocate for a systematic, linguistically informed approach to formulating adversarial questions, and we describe the results of our pilot experiments, as well as our official submission.

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ProtoTEx: Explaining Model Decisions with Prototype Tensors
Anubrata Das | Chitrank Gupta | Venelin Kovatchev | Matthew Lease | Junyi Jessy Li
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present ProtoTEx, a novel white-box NLP classification architecture based on prototype networks (Li et al., 2018). ProtoTEx faithfully explains model decisions based on prototype tensors that encode latent clusters of training examples. At inference time, classification decisions are based on the distances between the input text and the prototype tensors, explained via the training examples most similar to the most influential prototypes. We also describe a novel interleaved training algorithm that effectively handles classes characterized by ProtoTEx indicative features. On a propaganda detection task, ProtoTEx accuracy matches BART-large and exceeds BERTlarge with the added benefit of providing faithful explanations. A user study also shows that prototype-based explanations help non-experts to better recognize propaganda in online news.


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Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children’s mindreading ability
Venelin Kovatchev | Phillip Smith | Mark Lee | Rory Devine
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In this paper we implement and compare 7 different data augmentation strategies for the task of automatic scoring of children’s ability to understand others’ thoughts, feelings, and desires (or “mindreading”). We recruit in-domain experts to re-annotate augmented samples and determine to what extent each strategy preserves the original rating. We also carry out multiple experiments to measure how much each augmentation strategy improves the performance of automatic scoring systems. To determine the capabilities of automatic systems to generalize to unseen data, we create UK-MIND-20 - a new corpus of children’s performance on tests of mindreading, consisting of 10,320 question-answer pairs. We obtain a new state-of-the-art performance on the MIND-CA corpus, improving macro-F1-score by 6 points. Results indicate that both the number of training examples and the quality of the augmentation strategies affect the performance of the systems. The task-specific augmentations generally outperform task-agnostic augmentations. Automatic augmentations based on vectors (GloVe, FastText) perform the worst. We find that systems trained on MIND-CA generalize well to UK-MIND-20. We demonstrate that data augmentation strategies also improve the performance on unseen data.


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Decomposing and Comparing Meaning Relations: Paraphrasing, Textual Entailment, Contradiction, and Specificity
Venelin Kovatchev | Darina Gold | M. Antonia Marti | Maria Salamo | Torsten Zesch
Proceedings of the Twelfth Language Resources and Evaluation Conference

In this paper, we present a methodology for decomposing and comparing multiple meaning relations (paraphrasing, textual entailment, contradiction, and specificity). The methodology includes SHARel - a new typology that consists of 26 linguistic and 8 reason-based categories. We use the typology to annotate a corpus of 520 sentence pairs in English and we demonstrate that unlike previous typologies, SHARel can be applied to all relations of interest with a high inter-annotator agreement. We analyze and compare the frequency and distribution of the linguistic and reason-based phenomena involved in paraphrasing, textual entailment, contradiction, and specificity. This comparison allows for a much more in-depth analysis of the workings of the individual relations and the way they interact and compare with each other. We release all resources (typology, annotation guidelines, and annotated corpus) to the community.

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“What is on your mind?” Automated Scoring of Mindreading in Childhood and Early Adolescence
Venelin Kovatchev | Phillip Smith | Mark Lee | Imogen Grumley Traynor | Irene Luque Aguilera | Rory Devine
Proceedings of the 28th International Conference on Computational Linguistics

In this paper we present the first work on the automated scoring of mindreading ability in middle childhood and early adolescence. We create MIND-CA, a new corpus of 11,311 question-answer pairs in English from 1,066 children aged from 7 to 14. We perform machine learning experiments and carry out extensive quantitative and qualitative evaluation. We obtain promising results, demonstrating the applicability of state-of-the-art NLP solutions to a new domain and task.

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An Analysis of Natural Language Inference Benchmarks through the Lens of Negation
Md Mosharaf Hossain | Venelin Kovatchev | Pranoy Dutta | Tiffany Kao | Elizabeth Wei | Eduardo Blanco
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Negation is underrepresented in existing natural language inference benchmarks. Additionally, one can often ignore the few negations in existing benchmarks and still make the right inference judgments. In this paper, we present a new benchmark for natural language inference in which negation plays a critical role. We also show that state-of-the-art transformers struggle making inference judgments with the new pairs.


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A Qualitative Evaluation Framework for Paraphrase Identification
Venelin Kovatchev | M. Antonia Marti | Maria Salamo | Javier Beltran
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

In this paper, we present a new approach for the evaluation, error analysis, and interpretation of supervised and unsupervised Paraphrase Identification (PI) systems. Our evaluation framework makes use of a PI corpus annotated with linguistic phenomena to provide a better understanding and interpretation of the performance of various PI systems. Our approach allows for a qualitative evaluation and comparison of the PI models using human interpretable categories. It does not require modification of the training objective of the systems and does not place additional burden on the developers. We replicate several popular supervised and unsupervised PI systems. Using our evaluation framework we show that: 1) Each system performs differently with respect to a set of linguistic phenomena and makes qualitatively different kinds of errors; 2) Some linguistic phenomena are more challenging than others across all systems.

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Proceedings of the Student Research Workshop Associated with RANLP 2019
Venelin Kovatchev | Irina Temnikova | Branislava Šandrih | Ivelina Nikolova
Proceedings of the Student Research Workshop Associated with RANLP 2019

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RELATIONS - Workshop on meaning relations between phrases and sentences
Venelin Kovatchev | Darina Gold | Torsten Zesch
RELATIONS - Workshop on meaning relations between phrases and sentences

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Annotating and analyzing the interactions between meaning relations
Darina Gold | Venelin Kovatchev | Torsten Zesch
Proceedings of the 13th Linguistic Annotation Workshop

Pairs of sentences, phrases, or other text pieces can hold semantic relations such as paraphrasing, textual entailment, contradiction, specificity, and semantic similarity. These relations are usually studied in isolation and no dataset exists where they can be compared empirically. Here we present a corpus annotated with these relations and the analysis of these results. The corpus contains 520 sentence pairs, annotated with these relations. We measure the annotation reliability of each individual relation and we examine their interactions and correlations. Among the unexpected results revealed by our analysis is that the traditionally considered direct relationship between paraphrasing and bi-directional entailment does not hold in our data.


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ETPC - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation
Venelin Kovatchev | M. Antònia Martí | Maria Salamó
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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WARP-Text: a Web-Based Tool for Annotating Relationships between Pairs of Texts
Venelin Kovatchev | M. Antònia Martí | Maria Salamó
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

We present WARP-Text, an open-source web-based tool for annotating relationships between pairs of texts. WARP-Text supports multi-layer annotation and custom definitions of inter-textual and intra-textual relationships. Annotation can be performed at different granularity levels (such as sentences, phrases, or tokens). WARP-Text has an intuitive user-friendly interface both for project managers and annotators. WARP-Text fills a gap in the currently available NLP toolbox, as open-source alternatives for annotation of pairs of text are not readily available. WARP-Text has already been used in several annotation tasks and can be of interest to the researchers working in the areas of Paraphrasing, Entailment, Simplification, and Summarization, among others.


Proceedings of the Student Research Workshop Associated with RANLP 2017
Venelin Kovatchev | Irina Temnikova | Pepa Gencheva | Yasen Kiprov | Ivelina Nikolova
Proceedings of the Student Research Workshop Associated with RANLP 2017