Asher Stern


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The BIUTTE Research Platform for Transformation-based Textual Entailment Recognition
Asher Stern | Ido Dagan
Linguistic Issues in Language Technology, Volume 9, 2014 - Perspectives on Semantic Representations for Textual Inference

Recent progress in research of the Recognizing Textual Entailment (RTE) task shows a constantly-increasing level of complexity in this research field. A way to avoid having this complexity becoming a barrier for researchers, especially for new-comers in the field, is to provide a freely available RTE system with a high level of flexibility and extensibility. In this paper, we introduce our RTE system, BiuTee2, and suggest it as an effective research framework for RTE. In particular, BiuTee follows the prominent transformation-based paradigm for RTE, and offers an accessible platform for research within this approach. We describe each of BiuTee’s components and point out the mechanisms and properties which directly support adaptations and integration of new components. In addition, we describe BiuTee’s visual tracing tool, which provides notable assistance for researchers in refining and “debugging” their knowledge resources and inference components.

Recognizing Implied Predicate-Argument Relationships in Textual Inference
Asher Stern | Ido Dagan
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

The Excitement Open Platform for Textual Inferences
Bernardo Magnini | Roberto Zanoli | Ido Dagan | Kathrin Eichler | Guenter Neumann | Tae-Gil Noh | Sebastian Pado | Asher Stern | Omer Levy
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations


TruthTeller: Annotating Predicate Truth
Amnon Lotan | Asher Stern | Ido Dagan
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


Efficient Search for Transformation-based Inference
Asher Stern | Roni Stern | Ido Dagan | Ariel Felner
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

BIUTEE: A Modular Open-Source System for Recognizing Textual Entailment
Asher Stern | Ido Dagan
Proceedings of the ACL 2012 System Demonstrations


A Confidence Model for Syntactically-Motivated Entailment Proofs
Asher Stern | Ido Dagan
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011


A Resource for Investigating the Impact of Anaphora and Coreference on Inference.
Azad Abad | Luisa Bentivogli | Ido Dagan | Danilo Giampiccolo | Shachar Mirkin | Emanuele Pianta | Asher Stern
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Discourse phenomena play a major role in text processing tasks. However, so far relatively little study has been devoted to the relevance of discourse phenomena for inference. Therefore, an experimental study was carried out to assess the relevance of anaphora and coreference for Textual Entailment (TE), a prominent inference framework. First, the annotation of anaphoric and coreferential links in the RTE-5 Search data set was performed according to a specifically designed annotation scheme. As a result, a new data set was created where all anaphora and coreference instances in the entailing sentences which are relevant to the entailment judgment are solved and annotated.. A by-product of the annotation is a new “augmented” data set, where all the referring expressions which need to be resolved in the entailing sentences are replaced by explicit expressions. Starting from the final output of the annotation, the actual impact of discourse phenomena on inference engines was investigated, identifying the kind of operations that the systems need to apply to address discourse phenomena and trying to find direct mappings between these operation and annotation types.