Search Results for author:
Found 7 papers, 6 papers with code
Improving Factuality of Abstractive Summarization without Sacrificing Summary Quality
However, most of the prior works on training factuality-aware models have ignored the negative effect it has on summary quality.
IndicMT Eval: A Dataset to Meta-Evaluate Machine Translation metrics for Indian Languages
In this paper, we fill this gap by creating an MQM dataset consisting of 7000 fine-grained annotations, spanning 5 Indian languages and 7 MT systems, and use it to establish correlations between annotator scores and scores obtained using existing automatic metrics.
CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation
We present COunterfactual Generation via Retrieval and Editing (CORE), a retrieval-augmented generation framework for creating diverse counterfactual perturbations for CDA.
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions -- training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.
LatentGAN Autoencoder: Learning Disentangled Latent Distribution
In autoencoder, the encoder generally approximates the latent distribution over the dataset, and the decoder generates samples using this learned latent distribution.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Perturbation CheckLists for Evaluating NLG Evaluation Metrics
Natural Language Generation (NLG) evaluation is a multifaceted task requiring assessment of multiple desirable criteria, e. g., fluency, coherency, coverage, relevance, adequacy, overall quality, etc.
