Search Results for author:
Found 21 papers, 10 papers with code
Dynamic Facet Selection by Maximizing Graded Relevance
Dynamic faceted search (DFS), an interactive query refinement technique, is a form of Human–computer information retrieval (HCIR) approach.
Re2G: Retrieve, Rerank, Generate
As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger.
Ranked #1 on
Fact Verification
on KILT: FEVER
KGI: An Integrated Framework for Knowledge Intensive Language Tasks
In this paper, we present a system to showcase the capabilities of the latest state-of-the-art retrieval augmented generation models trained on knowledge-intensive language tasks, such as slot filling, open domain question answering, dialogue, and fact-checking.
End-to-End Table Question Answering via Retrieval-Augmented Generation
Most existing end-to-end Table Question Answering (Table QA) models consist of a two-stage framework with a retriever to select relevant table candidates from a corpus and a reader to locate the correct answers from table candidates.
Multi-Objective Design Space Exploration for the Optimization of the HEVC Mode Decision Process
Finding the best possible encoding decisions for compressing a video sequence is a highly complex problem.
Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation
In recent years, a number of keyphrase generation (KPG) approaches were proposed consisting of complex model architectures, dedicated training paradigms and decoding strategies.
Robust Retrieval Augmented Generation for Zero-shot Slot Filling
Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI.
Ranked #1 on
Zero-shot Slot Filling
on T-REx
CLTR: An End-to-End, Transformer-Based System for Cell-Level Table Retrieval and Table Question Answering
We present the first end-to-end, transformer-based table question answering (QA) system that takes natural language questions and massive table corpora as inputs to retrieve the most relevant tables and locate the correct table cells to answer the question.
AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry
Recent advances in transformers have enabled Table Question Answering (Table QA) systems to achieve high accuracy and SOTA results on open domain datasets like WikiTableQuestions and WikiSQL.
CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering
We present the first end-to-end, transformer-based table question answering (QA) system that takes natural language questions and massive table corpus as inputs to retrieve the most relevant tables and locate the correct table cells to answer the question.
Zero-shot Slot Filling with DPR and RAG
Recently, there has been a promising direction in evaluating language models in the same way we would evaluate knowledge bases, and the task of slot filling is the most suitable to this intent.
Capturing Row and Column Semantics in Transformer Based Question Answering over Tables
While this model yields extremely high accuracy at finding cell values on recent benchmarks, a second model we propose, called RCI representation, provides a significant efficiency advantage for online QA systems over tables by materializing embeddings for existing tables.
Scalable Hierarchical Clustering with Tree Grafting
We introduce Grinch, a new algorithm for large-scale, non-greedy hierarchical clustering with general linkage functions that compute arbitrary similarity between two point sets.
Frustratingly Easy Natural Question Answering
Existing literature on Question Answering (QA) mostly focuses on algorithmic novelty, data augmentation, or increasingly large pre-trained language models like XLNet and RoBERTa.
Ranked #5 on
Question Answering
on Natural Questions (long)
Span Selection Pre-training for Question Answering
BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA).
CFO: A Framework for Building Production NLP Systems
This paper introduces a novel orchestration framework, called CFO (COMPUTATION FLOW ORCHESTRATOR), for building, experimenting with, and deploying interactive NLP (Natural Language Processing) and IR (Information Retrieval) systems to production environments.
Learning Relational Representations by Analogy using Hierarchical Siamese Networks
We address relation extraction as an analogy problem by proposing a novel approach to learn representations of relations expressed by their textual mentions.
Discovering Implicit Knowledge with Unary Relations
State-of-the-art relation extraction approaches are only able to recognize relationships between mentions of entity arguments stated explicitly in the text and typically localized to the same sentence.
