Fatemeh Torabi Asr

Also published as: Fatemeh Torabi Asr


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The Data Challenge in Misinformation Detection: Source Reputation vs. Content Veracity
Fatemeh Torabi Asr | Maite Taboada
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

Misinformation detection at the level of full news articles is a text classification problem. Reliably labeled data in this domain is rare. Previous work relied on news articles collected from so-called “reputable” and “suspicious” websites and labeled accordingly. We leverage fact-checking websites to collect individually-labeled news articles with regard to the veracity of their content and use this data to test the cross-domain generalization of a classifier trained on bigger text collections but labeled according to source reputation. Our results suggest that reputation-based classification is not sufficient for predicting the veracity level of the majority of news articles, and that the system performance on different test datasets depends on topic distribution. Therefore collecting well-balanced and carefully-assessed training data is a priority for developing robust misinformation detection systems.

Querying Word Embeddings for Similarity and Relatedness
Fatemeh Torabi Asr | Robert Zinkov | Michael Jones
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Word embeddings obtained from neural network models such as Word2Vec Skipgram have become popular representations of word meaning and have been evaluated on a variety of word similarity and relatedness norming data. Skipgram generates a set of word and context embeddings, the latter typically discarded after training. We demonstrate the usefulness of context embeddings in predicting asymmetric association between words from a recently published dataset of production norms (Jouravlev & McRae, 2016). Our findings suggest that humans respond with words closer to the cue within the context embedding space (rather than the word embedding space), when asked to generate thematically related words.


An Artificial Language Evaluation of Distributional Semantic Models
Fatemeh Torabi Asr | Michael Jones
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Recent studies of distributional semantic models have set up a competition between word embeddings obtained from predictive neural networks and word vectors obtained from abstractive count-based models. This paper is an attempt to reveal the underlying contribution of additional training data and post-processing steps on each type of model in word similarity and relatedness inference tasks. We do so by designing an artificial language framework, training a predictive and a count-based model on data sampled from this grammar, and evaluating the resulting word vectors in paradigmatic and syntagmatic tasks defined with respect to the grammar.


Uniform Surprisal at the Level of Discourse Relations: Negation Markers and Discourse Connective Omission
Fatemeh Torabi Asr | Vera Demberg
Proceedings of the 11th International Conference on Computational Semantics


Conceptual and Practical Steps in Event Coreference Analysis of Large-scale Data
Fatemeh Torabi Asr | Jonathan Sonntag | Yulia Grishina | Manfred Stede
Proceedings of the Second Workshop on EVENTS: Definition, Detection, Coreference, and Representation


On the Information Conveyed by Discourse Markers
Fatemeh Torabi Asr | Vera Demberg
Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL)


Implicitness of Discourse Relations
Fatemeh Torabi Asr | Vera Demberg
Proceedings of COLING 2012

Measuring the Strength of Linguistic Cues for Discourse Relations
Fatemeh Torabi Asr | Vera Demberg
Proceedings of the Workshop on Advances in Discourse Analysis and its Computational Aspects