Search Results for author:
Found 41 papers, 16 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.
Permutation Invariant Strategy Using Transformer Encoders for Table Understanding
Existing methods that leverage pretrained Transformer encoders range from a simple construction of pseudo-sentences by concatenating text across rows or columns to complex parameter-intensive models that encode table structure and require additional pretraining.
Retrieval-Based Transformer for Table Augmentation
In this paper, we introduce a novel approach toward automatic data wrangling in an attempt to alleviate the effort of end-users, e. g. data analysts, in structuring dynamic views from data lakes in the form of tabular data.
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
Open-Domain Question Answering
on KILT: TriviaQA
Knowledge Graph Induction enabling Recommending and Trend Analysis: A Corporate Research Community Use Case
A research division plays an important role of driving innovation in an organization.
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.
A Generative Model for Relation Extraction and Classification
Relation extraction (RE) is an important information extraction task which provides essential information to many NLP applications such as knowledge base population and question answering.
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.
Semantic Answer Type and Relation Prediction Task (SMART 2021)
The Semantic Answer Type and Relation Prediction Task (SMART) task is one of the ISWC 2021 Semantic Web challenges.
Topic Transferable Table Question Answering
Weakly-supervised table question-answering(TableQA) models have achieved state-of-art performance by using pre-trained BERT transformer to jointly encoding a question and a table to produce structured query for the question.
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
Generative Relation Linking for Question Answering over Knowledge Bases
Relation linking is essential to enable question answering over knowledge bases.
Ranked #1 on
Relation Linking
on QALD-9
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.
A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering
Relation linking is a crucial component of Knowledge Base Question Answering systems.
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.
Type Prediction Systems
Inferring semantic types for entity mentions within text documents is an important asset for many downstream NLP tasks, such as Semantic Role Labelling, Entity Disambiguation, Knowledge Base Question Answering, etc.
Open Knowledge Graphs Canonicalization using Variational Autoencoders
In this work, we propose Canonicalizing Using Variational Autoencoders (CUVA), a joint model to learn both embeddings and cluster assignments in an end-to-end approach, which leads to a better vector representation for the noun and relation phrases.
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge
Each year the International Semantic Web Conference accepts a set of Semantic Web Challenges to establish competitions that will advance the state of the art solutions in any given problem domain.
Leveraging Semantic Parsing for Relation Linking over Knowledge Bases
Knowledgebase question answering systems are heavily dependent on relation extraction and linking modules.
Ranked #1 on
Relation Linking
on QALD-7
Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer
However, there has been no attempt to exploit GNN to create taxonomies.
Automatic Taxonomy Induction and Expansion
The Knowledge Graph Induction Service (KGIS) is an end-to-end knowledge induction system.
Hypernym Detection Using Strict Partial Order Networks
This paper introduces Strict Partial Order Networks (SPON), a novel neural network architecture designed to enforce asymmetry and transitive properties as soft constraints.
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).
Populating Web Scale Knowledge Graphs using Distantly Supervised Relation Extraction and Validation
In addition to that, the system uses a deep learning approach for knowledge base completion by utilizing the global structure information of the induced KG to further refine the confidence of the newly discovered relations.
Distributional Negative Sampling for Knowledge Base Completion
State-of-the-art approaches for Knowledge Base Completion (KBC) exploit deep neural networks trained with both false and true assertions: positive assertions are explicitly taken from the knowledge base, whereas negative ones are generated by random sampling of entities.
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.
A Dataset for Web-Scale Knowledge Base Population
Knowledge Base Population (KBP) is the task of building or extending a knowledge base from text, and systems for KBP have grown in capability and scope.
