Shabnam Behzad


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MultiMUC: Multilingual Template Filling on MUC-4
William Gantt | Shabnam Behzad | Hannah An | Yunmo Chen | Aaron White | Benjamin Van Durme | Mahsa Yarmohammadi
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

We introduce MultiMUC, the first multilingual parallel corpus for template filling, comprising translations of the classic MUC-4 template filling benchmark into five languages: Arabic, Chinese, Farsi, Korean, and Russian. We obtain automatic translations from a strong multilingual machine translation system and manually project the original English annotations into each target language. For all languages, we also provide human translations for key portions of the dev and test splits. Finally, we present baselines on MultiMUC both with state-of-the-art template filling models for MUC-4 and with ChatGPT. We release MUC-4 and the supervised baselines to facilitate further work on document-level information extraction in multilingual settings.


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Sentence-level Feedback Generation for English Language Learners: Does Data Augmentation Help?
Shabnam Behzad | Amir Zeldes | Nathan Schneider
Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges

In this paper, we present strong baselines for the task of Feedback Comment Generation for Writing Learning. Given a sentence and an error span, the task is to generate a feedback comment explaining the error. Sentences and feedback comments are both in English. We experiment with LLMs and also create multiple pseudo datasets for the task, investigating how it affects the performance of our system. We present our results for the task along with extensive analysis of the generated comments with the aim of aiding future studies in feedback comment generation for English language learners.

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ELQA: A Corpus of Metalinguistic Questions and Answers about English
Shabnam Behzad | Keisuke Sakaguchi | Nathan Schneider | Amir Zeldes
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present ELQA, a corpus of questions and answers in and about the English language. Collected from two online forums, the >70k questions (from English learners and others) cover wide-ranging topics including grammar, meaning, fluency, and etymology. The answers include descriptions of general properties of English vocabulary and grammar as well as explanations about specific (correct and incorrect) usage examples. Unlike most NLP datasets, this corpus is metalinguistic—it consists of language about language. As such, it can facilitate investigations of the metalinguistic capabilities of NLU models, as well as educational applications in the language learning domain. To study this, we define a free-form question answering task on our dataset and conduct evaluations on multiple LLMs (Large Language Models) to analyze their capacity to generate metalinguistic answers.

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GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation
Tatsuya Aoyama | Shabnam Behzad | Luke Gessler | Lauren Levine | Jessica Lin | Yang Janet Liu | Siyao Peng | Yilun Zhu | Amir Zeldes
Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)

We present GENTLE, a new mixed-genre English challenge corpus totaling 17K tokens and consisting of 8 unusual text types for out-of-domain evaluation: dictionary entries, esports commentaries, legal documents, medical notes, poetry, mathematical proofs, syllabuses, and threat letters. GENTLE is manually annotated for a variety of popular NLP tasks, including syntactic dependency parsing, entity recognition, coreference resolution, and discourse parsing. We evaluate state-of-the-art NLP systems on GENTLE and find severe degradation for at least some genres in their performance on all tasks, which indicates GENTLE’s utility as an evaluation dataset for NLP systems.

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The Effect of Alignment Correction on Cross-Lingual Annotation Projection
Shabnam Behzad | Seth Ebner | Marc Marone | Benjamin Van Durme | Mahsa Yarmohammadi
Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)

Cross-lingual annotation projection is a practical method for improving performance on low resource structured prediction tasks. An important step in annotation projection is obtaining alignments between the source and target texts, which enables the mapping of annotations across the texts. By manually correcting automatically generated alignments, we examine the impact of alignment quality—automatic, manual, and mixed—on downstream performance for two information extraction tasks and quantify the trade-off between annotation effort and model performance.


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DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection
Luke Gessler | Shabnam Behzad | Yang Janet Liu | Siyao Peng | Yilun Zhu | Amir Zeldes
Proceedings of the 2nd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2021)

This paper describes our submission to the DISRPT2021 Shared Task on Discourse Unit Segmentation, Connective Detection, and Relation Classification. Our system, called DisCoDisCo, is a Transformer-based neural classifier which enhances contextualized word embeddings (CWEs) with hand-crafted features, relying on tokenwise sequence tagging for discourse segmentation and connective detection, and a feature-rich, encoder-less sentence pair classifier for relation classification. Our results for the first two tasks outperform SOTA scores from the previous 2019 shared task, and results on relation classification suggest strong performance on the new 2021 benchmark. Ablation tests show that including features beyond CWEs are helpful for both tasks, and a partial evaluation of multiple pretrained Transformer-based language models indicates that models pre-trained on the Next Sentence Prediction (NSP) task are optimal for relation classification.

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Overview of AMALGUM – Large Silver Quality Annotations across English Genres
Luke Gessler | Siyao Peng | Yang Liu | Yilun Zhu | Shabnam Behzad | Amir Zeldes
Proceedings of the Society for Computation in Linguistics 2021


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A Cross-Genre Ensemble Approach to Robust Reddit Part of Speech Tagging
Shabnam Behzad | Amir Zeldes
Proceedings of the 12th Web as Corpus Workshop

Part of speech tagging is a fundamental NLP task often regarded as solved for high-resource languages such as English. Current state-of-the-art models have achieved high accuracy, especially on the news domain. However, when these models are applied to other corpora with different genres, and especially user-generated data from the Web, we see substantial drops in performance. In this work, we study how a state-of-the-art tagging model trained on different genres performs on Web content from unfiltered Reddit forum discussions. We report the results when training on different splits of the data, tested on Reddit. Our results show that even small amounts of in-domain data can outperform the contribution of data an order of magnitude larger coming from other Web domains. To make progress on out-of-domain tagging, we also evaluate an ensemble approach using multiple single-genre taggers as input features to a meta-classifier. We present state of the art performance on tagging Reddit data, as well as error analysis of the results of these models, and offer a typology of the most common error types among them, broken down by training corpus.

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AMALGUM – A Free, Balanced, Multilayer English Web Corpus
Luke Gessler | Siyao Peng | Yang Liu | Yilun Zhu | Shabnam Behzad | Amir Zeldes
Proceedings of the Twelfth Language Resources and Evaluation Conference

We present a freely available, genre-balanced English web corpus totaling 4M tokens and featuring a large number of high-quality automatic annotation layers, including dependency trees, non-named entity annotations, coreference resolution, and discourse trees in Rhetorical Structure Theory. By tapping open online data sources the corpus is meant to offer a more sizable alternative to smaller manually created annotated data sets, while avoiding pitfalls such as imbalanced or unknown composition, licensing problems, and low-quality natural language processing. We harness knowledge from multiple annotation layers in order to achieve a “better than NLP” benchmark and evaluate the accuracy of the resulting resource.

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Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and Data Augmentation in Neural Models for Propaganda Detection in News Articles
Michael Kranzlein | Shabnam Behzad | Nazli Goharian
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper presents our systems for SemEval 2020 Shared Task 11: Detection of Propaganda Techniques in News Articles. We participate in both the span identification and technique classification subtasks and report on experiments using different BERT-based models along with handcrafted features. Our models perform well above the baselines for both tasks, and we contribute ablation studies and discussion of our results to dissect the effectiveness of different features and techniques with the goal of aiding future studies in propaganda detection.