Search Results for author:
Found 20 papers, 8 papers with code
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?
Our analysis shows that it is challenging for the state-of-the-art multi-modal pre-trained models to answer visual information seeking questions, but this capability is improved through fine-tuning on the automated InfoSeek dataset.
Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities
Large-scale multi-modal pre-training models such as CLIP and PaLI exhibit strong generalization on various visual domains and tasks.
Promptagator: Few-shot Dense Retrieval From 8 Examples
To amplify the power of a few examples, we propose Prompt-base Query Generation for Retriever (Promptagator), which leverages large language models (LLM) as a few-shot query generator, and creates task-specific retrievers based on the generated data.
ASQA: Factoid Questions Meet Long-Form Answers
In contrast to existing long-form QA tasks (such as ELI5), ASQA admits a clear notion of correctness: a user faced with a good summary should be able to answer different interpretations of the original ambiguous question.
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each question needs to be interpreted within the dialogue context.
Large Dual Encoders Are Generalizable Retrievers
With multi-stage training, surprisingly, scaling up the model size brings significant improvement on a variety of retrieval tasks, especially for out-of-domain generalization.
Ranked #9 on
Zero-shot Text Search
on BEIR
Sparse, Dense, and Attentional Representations for Text Retrieval
Dual encoders perform retrieval by encoding documents and queries into dense lowdimensional vectors, scoring each document by its inner product with the query.
Contextualized Representations Using Textual Encyclopedic Knowledge
We present a method to represent input texts by contextualizing them jointly with dynamically retrieved textual encyclopedic background knowledge from multiple documents.
Entity, Relation, and Event Extraction with Contextualized Span Representations
We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction.
Ranked #5 on
Joint Entity and Relation Extraction
on SciERC
PaperRobot: Incremental Draft Generation of Scientific Ideas
We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper.
A General Framework for Information Extraction using Dynamic Span Graphs
We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs.
Ranked #1 on
Relation Extraction
on ACE 2004
(Cross Sentence metric)
Joint Entity and Relation Extraction
Named Entity Recognition (NER)
Text Generation from Knowledge Graphs with Graph Transformers
Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce.
Ranked #6 on
KG-to-Text Generation
on AGENDA
Monolingual sentence matching for text simplification
This work improves monolingual sentence alignment for text simplification, specifically for text in standard and simple Wikipedia.
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles.
Ranked #7 on
Joint Entity and Relation Extraction
on SciERC
Coreference Resolution
Joint Entity and Relation Extraction
+1
Scientific Relation Extraction with Selectively Incorporated Concept Embeddings
This paper describes our submission for the SemEval 2018 Task 7 shared task on semantic relation extraction and classification in scientific papers.
The UWNLP system at SemEval-2018 Task 7: Neural Relation Extraction Model with Selectively Incorporated Concept Embeddings
This paper describes our submission for SemEval 2018 Task 7 shared task on semantic relation extraction and classification in scientific papers.
Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models
Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training.
Scientific Information Extraction with Semi-supervised Neural Tagging
This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material.
LSTM based Conversation Models
In this paper, we present a conversational model that incorporates both context and participant role for two-party conversations.
