Papers by Tzuf Paz-Argaman
RUN through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation (D19-1)
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| Challenge: | Existing work on map-based NL navigation relies on small artificial worlds with a fixed set of entities known in advance. |
| Approach: | They propose a task to interpret navigation instructions in natural language (NL) they use a dataset aligned with real, dense, urban maps to study neural architectures . |
| Outcome: | The proposed task is based on a dataset of 2515 navigation instructions aligned with real routes over three regions of Manhattan. |
HeGeL: A Novel Dataset for Geo-Location from Hebrew Text (2023.findings-acl)
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| Challenge: | Existing datasets in English for textual geolocation are limited because of the location of the place is implicit. |
| Approach: | They propose to use a Hebrew place description corpus to analyze lingual geospatial reasoning. |
| Outcome: | The Hebrew Geo-Location corpus collects literal Hebrew place descriptions and analyzes lingual geospatial reasoning. |
Beyond N-Grams: Rethinking Evaluation Metrics and Strategies for Multilingual Abstractive Summarization (2025.acl-long)
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| Challenge: | n-gram-based metrics are considered indicative (even if imperfect) of human evaluation for English, but their suitability for other languages remains unclear. |
| Approach: | They systematically assess evaluation metrics for generation for languages and tasks using n-gram-based and neural-based metrics. |
| Outcome: | The proposed evaluation suite is based on eight languages from four typological families and shows that it is sensitivity to the language type at hand. |
Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs (2025.findings-naacl)
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| Challenge: | Current LLMs are primarily trained on English data but also include data from other languages. |
| Approach: | They propose to use a pre-translation strategy to translate a task prompt into English before inference . they use 'a modular entity' that could be translated into four different languages . |
| Outcome: | The proposed strategies are based on a set of pre-trained data across 35 languages covering both low and high-resource languages. |
Where Do We Go From Here? Multi-scale Allocentric Relational Inferencefrom Natural Spatial Descriptions (2024.eacl-long)
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| Challenge: | Current NLP navigation studies focus on egocentric local descriptions that require reasoning over the agent’s local perception. |
| Approach: | They propose to use a dataset to analyse English geospatial instructions to find locations and paths from natural language descriptions. |
| Outcome: | The proposed task and dataset includes 10,404 examples of English geospatial instructions for reaching a target location using map-knowledge. |
Into the Unknown: Generating Geospatial Descriptions for New Environments (2024.findings-acl)
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| Challenge: | Similar to vision-and-language navigation tasks, the Rendezvous (RVS) task requires reasoning over allocentric spatial relationships using non-sequential navigation instructions and maps. |
| Approach: | They propose a large-scale augmentation method for generating high-quality synthetic data for new environments using readily available geospatial data. |
| Outcome: | The proposed method improves accuracy on unseen and seen environments by 45.83% on the Rendezvous (RVS) task. |
HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew (2024.findings-acl)
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| Challenge: | Large language models excel in various natural language tasks in English, but their performance in low-resource languages like Hebrew remains unclear. |
| Approach: | They propose a benchmark dataset specifically designed for Hebrew abstractive text summarization that combines 10,000 article-summary pairs from Hebrew news websites. |
| Outcome: | The proposed dataset shows that it presents distinct difficulties even for state-of-the-art LLMs. |
ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization (2020.findings-emnlp)
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| Challenge: | Specifically, given birds’ images with free-text descriptions of their species, we learn to classify images of previously-unseen species based on specie descriptions. |
| Approach: | They propose to leverage the similarity between species and extract visual summaries from the texts to match visual features to the parts of the text that discuss them. |
| Outcome: | The proposed model outperforms the state-of-the-art on the largest benchmarks for text-based zero-shot learning. |