Search Results for author:
Found 12 papers, 7 papers with code
Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Structure Learning
Tree structured graphical models are powerful at expressing long range or hierarchical dependency among many variables, and have been widely applied in different areas of computer science and statistics.
Learning Recurrent Span Representations for Extractive Question Answering
In this paper, we focus on this answer extraction task, presenting a novel model architecture that efficiently builds fixed length representations of all spans in the evidence document with a recurrent network.
Ranked #43 on
Question Answering
on SQuAD1.1 dev
Improving Span-based Question Answering Systems with Coarsely Labeled Data
We study approaches to improve fine-grained short answer Question Answering models by integrating coarse-grained data annotated for paragraph-level relevance and show that coarsely annotated data can bring significant performance gains.
Natural Questions: a Benchmark for Question Answering Research
The public release consists of 307, 373 training examples with single annotations, 7, 830 examples with 5-way annotations for development data, and a further 7, 842 examples 5-way annotated sequestered as test data.
Ranked #7 on
Question Answering
on Natural Questions (long)
Handling Divergent Reference Texts when Evaluating Table-to-Text Generation
Automatically constructed datasets for generating text from semi-structured data (tables), such as WikiBio, often contain reference texts that diverge from the information in the corresponding semi-structured data.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
TaTa: A Multilingual Table-to-Text Dataset for African Languages
To address this lack of data, we create Table-to-Text in African languages (TaTa), the first large multilingual table-to-text dataset with a focus on African languages.
GPT-FinRE: In-context Learning for Financial Relation Extraction using Large Language Models
This paper describes our solution to relation extraction on one such dataset REFinD.
Nearest Neighbor Search over Vectorized Lexico-Syntactic Patterns for Relation Extraction from Financial Documents
However, existing RE models are usually incapable of handling two situations: implicit expressions and long-tail relation classes, caused by language complexity and data sparsity.
Birbal: An efficient 7B instruct-model fine-tuned with curated datasets
LLMOps incur significant costs due to hardware requirements, hindering their widespread accessibility.
Learning with Limited Text Data
Natural Language Processing (NLP) has achieved great progress in the past decade on the basis of neural models, which often make use of large amounts of labeled data to achieve state-of-the-art performance.
