Mitra Mohtarami


2021

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Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing
Hung-Yi Lee | Mitra Mohtarami | Shang-Wen Li | Di Jin | Mandy Korpusik | Shuyan Dong | Ngoc Thang Vu | Dilek Hakkani-Tur
Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing

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Interpretable Propaganda Detection in News Articles
Seunghak Yu | Giovanni Da San Martino | Mitra Mohtarami | James Glass | Preslav Nakov
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Online users today are exposed to misleading and propagandistic news articles and media posts on a daily basis. To counter thus, a number of approaches have been designed aiming to achieve a healthier and safer online news and media consumption. Automatic systems are able to support humans in detecting such content; yet, a major impediment to their broad adoption is that besides being accurate, the decisions of such systems need also to be interpretable in order to be trusted and widely adopted by users. Since misleading and propagandistic content influences readers through the use of a number of deception techniques, we propose to detect and to show the use of such techniques as a way to offer interpretability. In particular, we define qualitatively descriptive features and we analyze their suitability for detecting deception techniques. We further show that our interpretable features can be easily combined with pre-trained language models, yielding state-of-the-art results.

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Attentive Multiview Text Representation for Differential Diagnosis
Hadi Amiri | Mitra Mohtarami | Isaac Kohane
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We present a text representation approach that can combine different views (representations) of the same input through effective data fusion and attention strategies for ranking purposes. We apply our model to the problem of differential diagnosis, which aims to find the most probable diseases that match with clinical descriptions of patients, using data from the Undiagnosed Diseases Network. Our model outperforms several ranking approaches (including a commercially-supported system) by effectively prioritizing and combining representations obtained from traditional and recent text representation techniques. We elaborate on several aspects of our model and shed light on its improved performance.

2019

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SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums
Tsvetomila Mihaylova | Georgi Karadzhov | Pepa Atanasova | Ramy Baly | Mitra Mohtarami | Preslav Nakov
Proceedings of the 13th International Workshop on Semantic Evaluation

We present SemEval-2019 Task 8 on Fact Checking in Community Question Answering Forums, which features two subtasks. Subtask A is about deciding whether a question asks for factual information vs. an opinion/advice vs. just socializing. Subtask B asks to predict whether an answer to a factual question is true, false or not a proper answer. We received 17 official submissions for subtask A and 11 official submissions for Subtask B. For subtask A, all systems improved over the majority class baseline. For Subtask B, all systems were below a majority class baseline, but several systems were very close to it. The leaderboard and the data from the competition can be found at http://competitions.codalab.org/competitions/20022.

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Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection
Abdelrhman Saleh | Ramy Baly | Alberto Barrón-Cedeño | Giovanni Da San Martino | Mitra Mohtarami | Preslav Nakov | James Glass
Proceedings of the 13th International Workshop on Semantic Evaluation

We describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection. We rely on a variety of engineered features originally used to detect propaganda. This is based on the assumption that biased messages are propagandistic and promote a particular political cause or viewpoint. In particular, we trained a logistic regression model with features ranging from simple bag of words to vocabulary richness and text readability. Our system achieved 72.9% accuracy on the manually annotated testset, and 60.8% on the test data that was obtained with distant supervision. Additional experiments showed that significant performance gains can be achieved with better feature pre-processing.

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Contrastive Language Adaptation for Cross-Lingual Stance Detection
Mitra Mohtarami | James Glass | Preslav Nakov
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We study cross-lingual stance detection, which aims to leverage labeled data in one language to identify the relative perspective (or stance) of a given document with respect to a claim in a different target language. In particular, we introduce a novel contrastive language adaptation approach applied to memory networks, which ensures accurate alignment of stances in the source and target languages, and can effectively deal with the challenge of limited labeled data in the target language. The evaluation results on public benchmark datasets and comparison against current state-of-the-art approaches demonstrate the effectiveness of our approach.

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Neural Multi-Task Learning for Stance Prediction
Wei Fang | Moin Nadeem | Mitra Mohtarami | James Glass
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)

We present a multi-task learning model that leverages large amount of textual information from existing datasets to improve stance prediction. In particular, we utilize multiple NLP tasks under both unsupervised and supervised settings for the target stance prediction task. Our model obtains state-of-the-art performance on a public benchmark dataset, Fake News Challenge, outperforming current approaches by a wide margin.

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Vector of Locally Aggregated Embeddings for Text Representation
Hadi Amiri | Mitra Mohtarami
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We present Vector of Locally Aggregated Embeddings (VLAE) for effective and, ultimately, lossless representation of textual content. Our model encodes each input text by effectively identifying and integrating the representations of its semantically-relevant parts. The proposed model generates high quality representation of textual content and improves the classification performance of current state-of-the-art deep averaging networks across several text classification tasks.

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FAKTA: An Automatic End-to-End Fact Checking System
Moin Nadeem | Wei Fang | Brian Xu | Mitra Mohtarami | James Glass
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

We present FAKTA which is a unified framework that integrates various components of a fact-checking process: document retrieval from media sources with various types of reliability, stance detection of documents with respect to given claims, evidence extraction, and linguistic analysis. FAKTA predicts the factuality of given claims and provides evidence at the document and sentence level to explain its predictions.

2018

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Automatic Stance Detection Using End-to-End Memory Networks
Mitra Mohtarami | Ramy Baly | James Glass | Preslav Nakov | Lluís Màrquez | Alessandro Moschitti
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We present an effective end-to-end memory network model that jointly (i) predicts whether a given document can be considered as relevant evidence for a given claim, and (ii) extracts snippets of evidence that can be used to reason about the factuality of the target claim. Our model combines the advantages of convolutional and recurrent neural networks as part of a memory network. We further introduce a similarity matrix at the inference level of the memory network in order to extract snippets of evidence for input claims more accurately. Our experiments on a public benchmark dataset, FakeNewsChallenge, demonstrate the effectiveness of our approach.

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Integrating Stance Detection and Fact Checking in a Unified Corpus
Ramy Baly | Mitra Mohtarami | James Glass | Lluís Màrquez | Alessandro Moschitti | Preslav Nakov
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

A reasonable approach for fact checking a claim involves retrieving potentially relevant documents from different sources (e.g., news websites, social media, etc.), determining the stance of each document with respect to the claim, and finally making a prediction about the claim’s factuality by aggregating the strength of the stances, while taking the reliability of the source into account. Moreover, a fact checking system should be able to explain its decision by providing relevant extracts (rationales) from the documents. Yet, this setup is not directly supported by existing datasets, which treat fact checking, document retrieval, source credibility, stance detection and rationale extraction as independent tasks. In this paper, we support the interdependencies between these tasks as annotations in the same corpus. We implement this setup on an Arabic fact checking corpus, the first of its kind.

2016

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Learning Semantic Relatedness in Community Question Answering Using Neural Models
Henry Nassif | Mitra Mohtarami | James Glass
Proceedings of the 1st Workshop on Representation Learning for NLP

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SLS at SemEval-2016 Task 3: Neural-based Approaches for Ranking in Community Question Answering
Mitra Mohtarami | Yonatan Belinkov | Wei-Ning Hsu | Yu Zhang | Tao Lei | Kfir Bar | Scott Cyphers | Jim Glass
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Neural Attention for Learning to Rank Questions in Community Question Answering
Salvatore Romeo | Giovanni Da San Martino | Alberto Barrón-Cedeño | Alessandro Moschitti | Yonatan Belinkov | Wei-Ning Hsu | Yu Zhang | Mitra Mohtarami | James Glass
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In real-world data, e.g., from Web forums, text is often contaminated with redundant or irrelevant content, which leads to introducing noise in machine learning algorithms. In this paper, we apply Long Short-Term Memory networks with an attention mechanism, which can select important parts of text for the task of similar question retrieval from community Question Answering (cQA) forums. In particular, we use the attention weights for both selecting entire sentences and their subparts, i.e., word/chunk, from shallow syntactic trees. More interestingly, we apply tree kernels to the filtered text representations, thus exploiting the implicit features of the subtree space for learning question reranking. Our results show that the attention-based pruning allows for achieving the top position in the cQA challenge of SemEval 2016, with a relatively large gap from the other participants while greatly decreasing running time.

2015

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VectorSLU: A Continuous Word Vector Approach to Answer Selection in Community Question Answering Systems
Yonatan Belinkov | Mitra Mohtarami | Scott Cyphers | James Glass
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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A Vector Space Approach for Aspect Based Sentiment Analysis
Abdulaziz Alghunaim | Mitra Mohtarami | Scott Cyphers | Jim Glass
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

2013

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Probabilistic Sense Sentiment Similarity through Hidden Emotions
Mitra Mohtarami | Man Lan | Chew Lim Tan
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)