596 citations · 1.1k across the 37 of their papers we have counts for
43 papers
GisPy: A Tool for Measuring Gist Inference Score in Text
Pedram Hosseini, Christopher R. Wolfe, Mona Diab +1
Decision making theories such as Fuzzy-Trace Theory (FTT) suggest that individuals tend to rely on gist, or bottom-line meaning, in the text when making decisions. In this work, we…
Consistent Human Evaluation of Machine Translation across Language Pairs
Daniel Licht, Cynthia Gao, Janice Lam +3
Obtaining meaningful quality scores for machine translation systems through human evaluation remains a challenge given the high variability between human evaluators, partly due to…
Meta AI at Arabic Hate Speech 2022: MultiTask Learning with Self-Correction for Hate Speech Classification
Badr AlKhamissi, Mona Diab
In this paper, we tackle the Arabic Fine-Grained Hate Speech Detection shared task and demonstrate significant improvements over reported baselines for its three subtasks. The task…
A Review on Language Models as Knowledge Bases
Badr AlKhamissi, Millicent Li, Asli Celikyilmaz +2
Recently, there has been a surge of interest in the NLP community on the use of pretrained Language Models (LMs) as Knowledge Bases (KBs). Researchers have shown that LMs trained o…
Towards Responsible Natural Language Annotation for the Varieties of Arabic
A. Stevie Bergman, Mona T. Diab
When building NLP models, there is a tendency to aim for broader coverage, often overlooking cultural and (socio)linguistic nuance. In this position paper, we make the case for car…
CALCS 2021 Shared Task: Machine Translation for Code-Switched Data
Shuguang Chen, Gustavo Aguilar, Anirudh Srinivasan +2
To date, efforts in the code-switching literature have focused for the most part on language identification, POS, NER, and syntactic parsing. In this paper, we address machine tran…
Knowledge-Augmented Language Models for Cause-Effect Relation Classification
Pedram Hosseini, David A. Broniatowski, Mona Diab
Previous studies have shown the efficacy of knowledge augmentation methods in pretrained language models. However, these methods behave differently across domains and downstream ta…
Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs
Peter Hase, Mona Diab, Asli Celikyilmaz +5
Do language models have beliefs about the world? Dennett (1995) famously argues that even thermostats have beliefs, on the view that a belief is simply an informational state decou…
AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization
Alexander R. Fabbri, Xiaojian Wu, Srini Iyer +2
Community Question Answering (CQA) fora such as Stack Overflow and Yahoo! Answers contain a rich resource of answers to a wide range of community-based questions. Each question thr…
Discrete Cosine Transform as Universal Sentence Encoder
Nada Almarwani, Mona Diab
Modern sentence encoders are used to generate dense vector representations that capture the underlying linguistic characteristics for a sequence of words, including phrases, senten…
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data
Wei-Jen Ko, Ahmed El-Kishky, Adithya Renduchintala +6
The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages. Fortunately, some low-resource languages are lin…
Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation
Adithya Renduchintala, Denise Diaz, Kenneth Heafield +2
Is bias amplified when neural machine translation (NMT) models are optimized for speed and evaluated on generic test sets using BLEU? We investigate architectures and techniques co…
Predicting Directionality in Causal Relations in Text
Pedram Hosseini, David A. Broniatowski, Mona Diab
In this work, we test the performance of two bidirectional transformer-based language models, BERT and SpanBERT, on predicting directionality in causal pairs in the textual content…
Detecting Hallucinated Content in Conditional Neural Sequence Generation
Chunting Zhou, Graham Neubig, Jiatao Gu +4
Neural sequence models can generate highly fluent sentences, but recent studies have also shown that they are also prone to hallucinate additional content not supported by the inpu…
A Multitask Learning Approach for Diacritic Restoration
Sawsan Alqahtani, Ajay Mishra, Mona Diab
In many languages like Arabic, diacritics are used to specify pronunciations as well as meanings. Such diacritics are often omitted in written text, increasing the number of possib…
FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization
Esin Durmus, He He, Mona Diab
Neural abstractive summarization models are prone to generate content inconsistent with the source document, i.e. unfaithful. Existing automatic metrics do not capture such mistake…
Mutlitask Learning for Cross-Lingual Transfer of Semantic Dependencies
Maryam Aminian, Mohammad Sadegh Rasooli, Mona Diab
We describe a method for developing broad-coverage semantic dependency parsers for languages for which no semantically annotated resource is available. We leverage a multitask lear…
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking
Christopher Hidey, Tuhin Chakrabarty, Tariq Alhindi +4
The increased focus on misinformation has spurred development of data and systems for detecting the veracity of a claim as well as retrieving authoritative evidence. The Fact Extra…
Learning to Classify Intents and Slot Labels Given a Handful of Examples
Jason Krone, Yi Zhang, Mona Diab
Intent classification (IC) and slot filling (SF) are core components in most goal-oriented dialogue systems. Current IC/SF models perform poorly when the number of training example…
Efficient Convolutional Neural Networks for Diacritic Restoration
Sawsan Alqahtani, Ajay Mishra, Mona Diab
Diacritic restoration has gained importance with the growing need for machines to understand written texts. The task is typically modeled as a sequence labeling problem and current…
Homograph Disambiguation Through Selective Diacritic Restoration
Sawsan Alqahtani, Hanan Aldarmaki, Mona Diab
Lexical ambiguity, a challenging phenomenon in all natural languages, is particularly prevalent for languages with diacritics that tend to be omitted in writing, such as Arabic. Om…
Rumor Detection and Classification for Twitter Data
Sardar Hamidian, Mona T Diab
With the pervasiveness of online media data as a source of information verifying the validity of this information is becoming even more important yet quite challenging. Rumors spre…
Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues
Or Levi, Pedram Hosseini, Mona Diab +1
The blurry line between nefarious fake news and protected-speech satire has been a notorious struggle for social media platforms. Further to the efforts of reducing exposure to mis…
Overview for the Second Shared Task on Language Identification in Code-Switched Data
Giovanni Molina, Fahad AlGhamdi, Mahmoud Ghoneim +4
We present an overview of the second shared task on language identification in code-switched data. For the shared task, we had code-switched data from two different language pairs:…