40 citations · 124 across the 8 of their papers we have counts for
14 papers · 1 filter
Coreference Resolution through a seq2seq Transition-Based System
Bernd Bohnet, Chris Alberti, Michael Collins
Most recent coreference resolution systems use search algorithms over possible spans to identify mentions and resolve coreference. We instead present a coreference resolution syste…
Towards Computationally Verifiable Semantic Grounding for Language Models
Chris Alberti, Kuzman Ganchev, Michael Collins +2
The paper presents an approach to semantic grounding of language models (LMs) that conceptualizes the LM as a conditional model generating text given a desired semantic message for…
Conciseness: An Overlooked Language Task
Felix Stahlberg, Aashish Kumar, Chris Alberti +1
We report on novel investigations into training models that make sentences concise. We define the task and show that it is different from related tasks such as summarization and si…
Simple and Effective Gradient-Based Tuning of Sequence-to-Sequence Models
Jared Lichtarge, Chris Alberti, Shankar Kumar
Recent trends towards training ever-larger language models have substantially improved machine learning performance across linguistic tasks. However, the huge cost of training larg…
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Sewon Min, Jordan Boyd-Graber, Chris Alberti +50
We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and…
Data Weighted Training Strategies for Grammatical Error Correction
Jared Lichtarge, Chris Alberti, Shankar Kumar
Recent progress in the task of Grammatical Error Correction (GEC) has been driven by addressing data sparsity, both through new methods for generating large and noisy pretraining d…