Yoad Winter


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SpaceNLI: Evaluating the Consistency of Predicting Inferences In Space
Lasha Abzianidze | Joost Zwarts | Yoad Winter
Proceedings of the 4th Natural Logic Meets Machine Learning Workshop

While many natural language inference (NLI) datasets target certain semantic phenomena, e.g., negation, tense & aspect, monotonicity, and presupposition, to the best of our knowledge, there is no NLI dataset that involves diverse types of spatial expressions and reasoning. We fill this gap by semi-automatically creating an NLI dataset for spatial reasoning, called SpaceNLI. The data samples are automatically generated from a curated set of reasoning patterns (see Figure 1), where the patterns are annotated with inference labels by experts. We test several SOTA NLI systems on SpaceNLI to gauge the complexity of the dataset and the system’s capacity for spatial reasoning. Moreover, we introduce a Pattern Accuracy and argue that it is a more reliable and stricter measure than the accuracy for evaluating a system’s performance on pattern-based generated data samples. Based on the evaluation results we find that the systems obtain moderate results on the spatial NLI problems but lack consistency per inference pattern. The results also reveal that non-projective spatial inferences (especially due to the “between” preposition) are the most challenging ones.


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Geo-Aware Image Caption Generation
Sofia Nikiforova | Tejaswini Deoskar | Denis Paperno | Yoad Winter
Proceedings of the 28th International Conference on Computational Linguistics

Standard image caption generation systems produce generic descriptions of images and do not utilize any contextual information or world knowledge. In particular, they are unable to generate captions that contain references to the geographic context of an image, for example, the location where a photograph is taken or relevant geographic objects around an image location. In this paper, we develop a geo-aware image caption generation system, which incorporates geographic contextual information into a standard image captioning pipeline. We propose a way to build an image-specific representation of the geographic context and adapt the caption generation network to produce appropriate geographic names in the image descriptions. We evaluate our system on a novel captioning dataset that contains contextualized captions and geographic metadata and achieve substantial improvements in BLEU, ROUGE, METEOR and CIDEr scores. We also introduce a new metric to assess generated geographic references directly and empirically demonstrate our system’s ability to produce captions with relevant and factually accurate geographic referencing.


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Presupposition Projection and Repair Strategies in Trivalent Semantics
Yoad Winter
Proceedings of the 16th Meeting on the Mathematics of Language


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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.


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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


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Smoothing a Lexicon-based POS Tagger for Arabic and Hebrew
Saib Manour | Khalil Sima’an | Yoad Winter
Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources


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Controlled Language for Geographical Information System Queries
Sela Mador-Haim | Yoad Winter | Anthony Braun
Proceedings of the Fifth International Workshop on Inference in Computational Semantics (ICoS-5)


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Choosing an Optimal Architecture for Segmentation and POS-Tagging of Modern Hebrew
Roy Bar-Haim | Khalil Sima’an | Yoad Winter
Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages