Search Results for author:
Found 20 papers, 8 papers with code
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.
KnowGL: Knowledge Generation and Linking from Text
We propose KnowGL, a tool that allows converting text into structured relational data represented as a set of ABox assertions compliant with the TBox of a given Knowledge Graph (KG), such as Wikidata.
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.
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.
A Two-Stage Approach towards Generalization in Knowledge Base Question Answering
Most existing approaches for Knowledge Base Question Answering (KBQA) focus on a specific underlying knowledge base either because of inherent assumptions in the approach, or because evaluating it on a different knowledge base requires non-trivial changes.
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
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.
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.
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
Exploring Software Naturalness through Neural Language Models
We explore this hypothesis through the use of a pre-trained transformer-based language model to perform code analysis tasks.
Knowledge Graph Embeddings and Explainable AI
Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces.
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.
Centroid-based Text Summarization through Compositionality of Word Embeddings
The textual similarity is a crucial aspect for many extractive text summarization methods.
Iterative Multi-document Neural Attention for Multiple Answer Prediction
People have information needs of varying complexity, which can be solved by an intelligent agent able to answer questions formulated in a proper way, eventually considering user context and preferences.
