Papers by Elad Venezian
Automatic Argument Quality Assessment - New Datasets and Methods (D19-1)
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Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
| Challenge: | 6.3k arguments were collected from contributors of various levels, and are released as part of this work. |
| Approach: | They propose to use a language model to annotate arguments for argument ranking and argument-pair classification. |
| Outcome: | The proposed methods outperform state-of-the-art methods in the argument ranking task and argument-pair classification task. |
Knowledge is a Region in Weight Space for Fine-tuned Language Models (2023.findings-emnlp)
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| Challenge: | Specifically, language models that have been finetuned on the same dataset form a tight cluster in the weight space, while models finetuning on different datasets from the same underlying task form s looser clusters. |
| Approach: | They analyze the weight space and the underlying loss landscape of different models to determine how they are interconnected and how they perform. |
| Outcome: | The proposed method improves accuracy on 11 out of 12 datasets by starting from the center of the region, if not more, than using the pretrained model. |
Learning Thematic Similarity Metric from Article Sections Using Triplet Networks (P18-2)
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| Challenge: | In this paper, we use Wikipedia articles to learn thematic similarity metric between sentences. |
| Approach: | They propose to leverage the partition of articles into sections to learn thematic similarity metric between sentences. |
| Outcome: | The proposed model outperforms state-of-the-art embeddings on the task of thematic clustering of sentences. |
Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI (2024.naacl-demo)
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Elron Bandel, Yotam Perlitz, Elad Venezian, Roni Friedman, Ofir Arviv, Matan Orbach, Shachar Don-Yehiya, Dafna Sheinwald, Ariel Gera, Leshem Choshen, Michal Shmueli-Scheuer, Yoav Katz
| Challenge: | Textual data processing pipelines are tailored to specific datasets, task and model combinations. |
| Approach: | They propose a library for customizable textual data preparation and evaluation tailored to generative language models. |
| Outcome: | Unitxt is a library for customizable textual data preparation and evaluation tailored to generative language models. |
Advances in Debating Technologies: Building AI That Can Debate Humans (2021.acl-tutorials)
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| Challenge: | This tutorial focuses on Debating Technologies, a sub-field of computational argumentation defined as "computational technologies developed directly to enhance, support, and engage with human debating" the tutorial provides a holistic view of a debated system, and discusses practical applications and future challenges of debation technologies. |
| Approach: | They present a tutorial on Debating Technologies, a sub-field of computational argumentation . they introduce Project Debater, which is the first AI system to debate human experts . |
| Outcome: | The project Debater is the first AI system to debate human experts on complex topics. |
ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning (2023.acl-long)
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| Challenge: | Pretraining has been shown to scale well with compute, data size and data diversity. |
| Approach: | They propose a method that provides benefits of multitask learning but leverages distributed computation . they propose 'coldfusion' can create synergistic loop where finetuned models can be "recycled" |
| Outcome: | The proposed method outperforms RoBERTa and previous multitask models on 35 datasets. |
A Recorded Debating Dataset (L18-1)
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Shachar Mirkin, Michal Jacovi, Tamar Lavee, Hong-Kwang Kuo, Samuel Thomas, Leslie Sager, Lili Kotlerman, Elad Venezian, Noam Slonim
| Challenge: | Existing research in computational argumentation and debating technologies focuses on argumentation mining, but other tasks are being addressed as well. |
| Approach: | They describe a dataset of debating speeches in English that is used for research . they use an automatic speech recognition system to produce a more "nLP-friendly" text . |
| Outcome: | The proposed dataset contains 60 speeches on various controversial topics, each in five formats corresponding to different stages in production. |
Where to start? Analyzing the potential value of intermediate models (2023.emnlp-main)
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| Challenge: | a finetuned model may be better base models than the vanilla pretrained model . this scheme, often referred to as intertraining, is the focus of the present work . |
| Approach: | They propose a scheme to analyze the potential intertraining gain independently for the target dataset and for a base model being considered as a starting point. |
| Outcome: | The proposed model is strong even if training data was not aligned with target dataset. |
Project Debater APIs: Decomposing the AI Grand Challenge (2021.emnlp-demo)
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| Challenge: | Project Debater is the first AI system that can debate human experts on complex topics. |
| Approach: | They describe Project Debater's architecture and evaluate its performance . they will focus on Key Point Analysis, a novel technology that identifies main points . |
| Outcome: | The proposed system can debate human experts on complex topics. |