Search Results for author:
Found 7 papers, 6 papers with code
Measuring the Robustness of NLP Models to Domain Shifts
Our benchmark focuses on natural topical domain shifts and enables measuring both the SD and the TD.
On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method
We find that high benchmark scores do not necessarily translate to strong robustness, and that various methods can perform extremely differently under different settings.
PASS: Perturb-and-Select Summarizer for Product Reviews
We propose the PASS system (Perturb-and-Select Summarizer) that employs a large pre-trained Transformer-based model (T5 in our case), which follows a few-shot fine-tuning scheme.
PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains
We address a challenging and underexplored version of this domain adaptation problem, where an algorithm is trained on several source domains, and then applied to examples from unseen domains that are unknown at training time.
CausaLM: Causal Model Explanation Through Counterfactual Language Models
Concretely, we show that by carefully choosing auxiliary adversarial pre-training tasks, language representation models such as BERT can effectively learn a counterfactual representation for a given concept of interest, and be used to estimate its true causal effect on model performance.
Bidding in Spades
We present a Spades bidding algorithm that is superior to recreational human players and to publicly available bots.
Predicting In-game Actions from Interviews of NBA Players
We find that our best performing textual model is most associated with topics that are intuitively related to each prediction task and that better models yield higher correlation with more informative topics.
