Yonatan Bisk

Evaluating Induced CCG Parsers on Grounded Semantic Parsing

2016

We compare the effectiveness of four different syntactic CCG parsers for a semantic slotfilling task to explore how much syntactic supervision is required for downstream semantic analysis. This extrinsic, task-based evaluation also provides a unique …

Character-based Surprisal as a Model of Reading Difficulty in the Presence of Errors

2019

Intuitively, human readers cope easily with errors in text; typos, misspelling, word substitutions, etc. do not unduly disrupt natural reading. Previous work indicates that letter transpositions result in increased reading times, but it is unclear …

Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward

2025

Ruohong Zhang, Liangke Gui, Zhiqing Sun, Yihao Feng, Keyang Xu, Yuanhan Zhang, Di Fu, Chunyuan Li, Alexander G Hauptmann, Yonatan Bisk, Yiming Yang. Proceedings of the 2025 Conference of the Nations of the Americas Chapter …

MolErr2Fix: Benchmarking LLM Trustworthiness in Chemistry via Modular Error Detection, Localization, Explanation, and Revision

2025 · arXiv (Cornell University)

Large Language Models (LLMs) have shown growing potential in molecular sciences, but they often produce chemically inaccurate descriptions and struggle to recognize or justify potential errors. This raises important concerns about their robustness and reliability …

Supertagging With LSTMs

2016

Ashish Vaswani, Yonatan Bisk, Kenji Sagae, Ryan Musa. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.

Synthetic and Natural Noise Both Break Neural Machine Translation

2017 · arXiv (Cornell University)

Character-based neural machine translation (NMT) models alleviate out-of-vocabulary issues, learn morphology, and move us closer to completely end-to-end translation systems. Unfortunately, they are also very brittle and easily falter when presented with noisy data. In …

SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference

2018

Given a partial description like "she opened the hood of the car," humans can reason about the situation and anticipate what might come next ("then, she examined the engine"). In this paper, we introduce the …

HellaSwag: Can a Machine Really Finish Your Sentence?

2019

introduced a new task of commonsense natural language inference: given an event description such as "A woman sits at a piano," a machine must select the most likely followup: "She sets her fingers on the …

Synthetic and Natural Noise Both Break Neural Machine Translation

2018 · International Conference on Learning Representations

Character-based neural machine translation (NMT) models alleviate out-of-vocabulary issues, learn morphology, and move us closer to completely end-to-end translation systems. Unfortunately, they are also very brittle and easily falter when presented with noisy data. In …

Unsupervised Neural Hidden Markov Models

2016

In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model. We evaluate our approach on tag induction. Our approach outperforms existing generative models and is competitive with the state-of-the-art though …

PIQA: Reasoning about Physical Commonsense in Natural Language

2020

To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions requiring this kind of physical commonsense pose a challenge to today's natural language understanding systems. While recent pretrained models …