Alan Ritter

Shared Tasks of the 2015 Workshop on Noisy User-generated Text: Twitter Lexical Normalization and Named Entity Recognition

2015 · The Association for Computational Linguistics

This paper presents the results of the two shared tasks associated with W-NUT 2015: (1) a text normalization task with 10 participants; and (2) a named entity tagging task with 8 participants. We outline the …

Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding

2023 · arXiv (Cornell University)

Language style is often used by writers to convey their intentions, identities, and mastery of language. In this paper, we show that current large language models struggle to capture some language styles without fine-tuning. To …

Language Models can Self-Improve at State-Value Estimation for Better Search

2025

Collecting ground-truth rewards or human demonstrations for multi-step reasoning tasks is often prohibitively expensive, particularly in interactive domains such as web tasks. We introduce Self-Taught Lookahead (STL), a reward-free framework that improves language model-based value …

Auditing Language Model Unlearning via Information Decomposition

2026 · TUbilio (Technical University of Darmstadt)

We expose a critical limitation in current approaches to machine unlearning in language models: despite the apparent success of unlearning algorithms, information about the forgotten data remains linearly decodable from internal representations. To systematically assess …

Adversarial Learning for Neural Dialogue Generation

2017 · arXiv (Cornell University)

In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the …

Results of the WNUT16 Named Entity Recognition Shared Task

2016 · Digital Access to Libraries

This paper presents the results of the Twitter Named Entity Recognition shared task associated with W-NUT 2016: a named entity tagging task with 10 teams participating. We outline the shared task, annotation process and dataset …

An Empirical Study of Pre-trained Transformers for Arabic Information Extraction

2020

Multilingual pre-trained Transformers, such as mBERT However, their performance on Arabic information extraction (IE) tasks is not very well studied. In this paper, we pre-train a customized bilingual BERT, dubbed GigaBERT, that is designed specifically …