Search Results for author:
Found 12 papers, 10 papers with code
CodeBenchGen: Creating Scalable Execution-based Code Generation Benchmarks
We will release the code of both the framework and the dataset upon acceptance.
Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries
Summarizing clinical text is crucial in health decision-support and clinical research.
DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation
Medical text generation aims to assist with administrative work and highlight salient information to support decision-making.
Data Augmentation for Code Translation with Comparable Corpora and Multiple References
One major challenge of translating code between programming languages is that parallel training data is often limited.
Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers
This paper explores the effectiveness of model-generated signals in improving zero-shot generalization of text-to-text Transformers such as T5.
Unsupervised Dense Retrieval Training with Web Anchors
Based on their commonalities, we train an unsupervised dense retriever, Anchor-DR, with a contrastive learning task that matches the anchor text and the linked document.
Open-Vocabulary Argument Role Prediction for Event Extraction
Specifically, we formulate the role prediction problem as an in-filling task and construct prompts for a pre-trained language model to generate candidate roles.
P4E: Few-Shot Event Detection as Prompt-Guided Identification and Localization
Our framework decomposes event detection into an identification task and a localization task.
Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion
Typical DocRE methods blindly take the full document as input, while a subset of the sentences in the document, noted as the evidence, are often sufficient for humans to predict the relation of an entity pair.
Ranked #5 on
Relation Extraction
on DocRED
CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring
Taxonomy is not only a fundamental form of knowledge representation, but also crucial to vast knowledge-rich applications, such as question answering and web search.
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning
Additionally, the explicit redundancy measure in MMR helps the neural representation of the summary to better capture redundancy.
Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion
Given a small set of seed entities (e. g., ``USA'', ``Russia''), corpus-based set expansion is to induce an extensive set of entities which share the same semantic class (Country in this example) from a given corpus.
