Assaf Toledo


2020

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Out of the Echo Chamber: Detecting Countering Debate Speeches
Matan Orbach | Yonatan Bilu | Assaf Toledo | Dan Lahav | Michal Jacovi | Ranit Aharonov | Noam Slonim
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

An educated and informed consumption of media content has become a challenge in modern times. With the shift from traditional news outlets to social media and similar venues, a major concern is that readers are becoming encapsulated in “echo chambers” and may fall prey to fake news and disinformation, lacking easy access to dissenting views. We suggest a novel task aiming to alleviate some of these concerns – that of detecting articles that most effectively counter the arguments – and not just the stance – made in a given text. We study this problem in the context of debate speeches. Given such a speech, we aim to identify, from among a set of speeches on the same topic and with an opposing stance, the ones that directly counter it. We provide a large dataset of 3,685 such speeches (in English), annotated for this relation, which hopefully would be of general interest to the NLP community. We explore several algorithms addressing this task, and while some are successful, all fall short of expert human performance, suggesting room for further research. All data collected during this work is freely available for research.

2019

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Syntactic Interchangeability in Word Embedding Models
Daniel Hershcovich | Assaf Toledo | Alon Halfon | Noam Slonim
Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP

Nearest neighbors in word embedding models are commonly observed to be semantically similar, but the relations between them can vary greatly. We investigate the extent to which word embedding models preserve syntactic interchangeability, as reflected by distances between word vectors, and the effect of hyper-parameters—context window size in particular. We use part of speech (POS) as a proxy for syntactic interchangeability, as generally speaking, words with the same POS are syntactically valid in the same contexts. We also investigate the relationship between interchangeability and similarity as judged by commonly-used word similarity benchmarks, and correlate the result with the performance of word embedding models on these benchmarks. Our results will inform future research and applications in the selection of word embedding model, suggesting a principle for an appropriate selection of the context window size parameter depending on the use-case.

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Learning to combine Grammatical Error Corrections
Yoav Kantor | Yoav Katz | Leshem Choshen | Edo Cohen-Karlik | Naftali Liberman | Assaf Toledo | Amir Menczel | Noam Slonim
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

The field of Grammatical Error Correction (GEC) has produced various systems to deal with focused phenomena or general text editing. We propose an automatic way to combine black-box systems. Our method automatically detects the strength of a system or the combination of several systems per error type, improving precision and recall while optimizing F-score directly. We show consistent improvement over the best standalone system in all the configurations tested. This approach also outperforms average ensembling of different RNN models with random initializations. In addition, we analyze the use of BERT for GEC - reporting promising results on this end. We also present a spellchecker created for this task which outperforms standard spellcheckers tested on the task of spellchecking. This paper describes a system submission to Building Educational Applications 2019 Shared Task: Grammatical Error Correction. Combining the output of top BEA 2019 shared task systems using our approach, currently holds the highest reported score in the open phase of the BEA 2019 shared task, improving F-0.5 score by 3.7 points over the best result reported.

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Automatic Argument Quality Assessment - New Datasets and Methods
Assaf Toledo | Shai Gretz | Edo Cohen-Karlik | Roni Friedman | Elad Venezian | Dan Lahav | Michal Jacovi | Ranit Aharonov | Noam Slonim
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 explore the task of automatic assessment of argument quality. To that end, we actively collected 6.3k arguments, more than a factor of five compared to previously examined data. Each argument was explicitly and carefully annotated for its quality. In addition, 14k pairs of arguments were annotated independently, identifying the higher quality argument in each pair. In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results. We release the labeled datasets to the community. Furthermore, we suggest neural methods based on a recently released language model, for argument ranking as well as for argument-pair classification. In the former task, our results are comparable to state-of-the-art; in the latter task our results significantly outperform earlier methods.

2014

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Annotating by Proving using SemAnTE
Assaf Toledo | Stavroula Alexandropoulou | Sophie Chesney | Robert Grimm | Pepijn Kokke | Benno Kruit | Kyriaki Neophytou | Antony Nguyen | Yoad Winter
Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Towards a Semantic Model for Textual Entailment Annotation
Assaf Toledo | Stavroula Alexandropoulou | Sophie Chesney | Sophia Katrenko | Heid Klockmann | Pepjin Kokke | Benno Kruit | Yoad Winter
Linguistic Issues in Language Technology, Volume 9, 2014 - Perspectives on Semantic Representations for Textual Inference

We introduce a new formal semantic model for annotating textual entailments that describes restrictive, intersective, and appositive modification. The model contains a formally defined interpreted lexicon, which specifies the inventory of symbols and the supported semantic operators, and an informally defined annotation scheme that instructs annotators in which way to bind words and constructions from a given pair of premise and hypothesis to the interpreted lexicon. We explore the applicability of the proposed model to the Recognizing Textual Entailment (RTE) 1–4 corpora and describe a first-stage annotation scheme on which we based the manual annotation work. The constructions we annotated were found to occur in 80.65% of the entailments in RTE 1–4 and were annotated with cross-annotator agreement of 68% on average. The annotated parts of the RTE corpora are publicly available for further research.

2013

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Semantic Annotation of Textual Entailment
Assaf Toledo | Stavroula Alexandropoulou | Sophia Katrenko | Heidi Klockmann | Pepijn Kokke | Yoad Winter
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers