Papers by Hagai Taitelbaum

6 papers
Multilingual word translation using auxiliary languages (D19-1)

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Challenge: Existing multilingual word translation methods focus on learning mappings from each language to a shared space.
Approach: They propose a multilingual translation procedure that uses all the learned mappings to translate a word from one language to another.
Outcome: Experiments on a standard multilingual word translation benchmark show that the proposed translation procedure outperforms state-of-the-art translation methods.
A Multi-Pairwise Extension of Procrustes Analysis for Multilingual Word Translation (D19-1)

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Challenge: Existing approaches to multilingual word embeddings require a k-way dictionary.
Approach: They propose a novel approach to simultaneously representing multiple languages in a common space by using a pairwise bilingual dictionary.
Outcome: The proposed approach requires only pairwise bilingual dictionaries that are much easier to construct.
A Locally Linear Procedure for Word Translation (2020.coling-main)

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Challenge: Existing methods to learn word embeddings of two languages are limited by the expressiveness of the translation model.
Approach: They propose an algorithm that uses multiple orthogonal translation matrices to model the mapping and derive an algorithm to learn these multiple matric.
Outcome: The proposed algorithm achieves better performance in bilingual and cross-lingual word translation tasks compared to the single matrix baseline.
On Reference (In-)Determinacy in Natural Language Inference (2025.findings-naacl)

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Challenge: Using reference determinacy, models fail to recognize context mismatch in NLI examples .
Approach: They propose a benchmark to identify reference ambiguity in NLI examples . they propose RD as a possible assumption for natural language inference .
Outcome: The proposed benchmark identifies reference ambiguity in natural language inference examples . 80% false contradiction and >50% entailment predictions are found .
TRUE: Re-evaluating Factual Consistency Evaluation (2022.naacl-main)

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Challenge: Grounded text generation systems often generate factual inconsistencies, hindering their real-world applicability.
Approach: They propose a method to assess factual consistency metrics on standardized texts . they recommend NLI and question generation-and-answering-based methods as starting points .
Outcome: The proposed method is more actionable and interpretable than previous methods.
RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation (2025.findings-emnlp)

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Challenge: Existing methods assess only one aspect of the task, misalign with human judgments or rely on costly API-based evaluation.
Approach: RefVNLI evaluates textual alignment and subject preservation in a single run.
Outcome: RefVNLI outperforms or matches existing baselines across multiple benchmarks and subject categories.

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