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Found 17 papers, 0 papers with code
Unsupervised Generation of Long-form Technical Questions from Textbook Metadata using Structured Templates
Semi-structured metadata of a textbook — the table of contents and the index — provide rich cues for technical question generation.
Discovering Knowledge Graph Schema from Short Natural Language Text via Dialog
For such mini-dialogs with response uncertainty, we propose a dialog strategy that looks to elicit the schema over as short a dialog as possible.
A Weak Supervision Approach for Predicting Difficulty of Technical Interview Questions
Predicting difficulty of questions is crucial for technical interviews.
RETINAQA : A Knowledge Base Question Answering Model Robust to both Answerable and Unanswerable Questions
We propose a new model for KBQA named RetinaQA that is robust against unaswerability.
Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning
Additional experiments in the in-domain setting show that FuSIC-KBQA also outperforms SoTA KBQA models when training data is limited.
Adapting Pre-trained Generative Models for Extractive Question Answering
Pre-trained Generative models such as BART, T5, etc.
Extractive Question-Answering
Long Form Question Answering
+1
Do the Benefits of Joint Models for Relation Extraction Extend to Document-level Tasks?
Joint models, which capture interactions across triples, are the more recent development, and have been shown to outperform pipeline models for sentence-level extraction tasks.
90% F1 Score in Relational Triple Extraction: Is it Real ?
This approach leads to overall performance improvement in these models within the realistic experimental setting.
Ranked #11 on
Relation Extraction
on NYT
Do I have the Knowledge to Answer? Investigating Answerability of Knowledge Base Questions
When answering natural language questions over knowledge bases, missing facts, incomplete schema and limited scope naturally lead to many questions being unanswerable.
Generating An Optimal Interview Question Plan Using A Knowledge Graph And Integer Linear Programming
Given the diversity of the candidates and complexity of job requirements, and since interviewing is an inherently subjective process, it is an important task to ensure consistent, uniform, efficient and objective interviews that result in high quality recruitment.
Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network
Existing approaches for table annotation with entities and types either capture the structure of table using graphical models, or learn embeddings of table entries without accounting for the complete syntactic structure.
Complex Question Answering on knowledge graphs using machine translation and multi-task learning
Question answering (QA) over a knowledge graph (KG) is a task of answering a natural language (NL) query using the information stored in KG.
Enabling human-like task identification from natural conversation
In this work, we provide a non-trivial method to combine an NLP engine and a planner such that a robot can successfully identify tasks and all the relevant parameters and generate an accurate plan for the task.
Discovering Topical Interactions in Text-based Cascades using Hidden Markov Hawkes Processes
We show using experiments on real and semi-synthetic data that HMHP is able to generalize better and recover the network strengths, topics and diffusion paths more accurately than state-of-the-art baselines.
Stance Classification of Context-Dependent Claims
Recent work has addressed the problem of detecting relevant claims for a given controversial topic.
Nested Hierarchical Dirichlet Processes for Multi-Level Non-Parametric Admixture Modeling
The Hierarchical Dirichlet Process (HDP), is an extension of DP for grouped data, often used for non-parametric topic modeling, where each group is a mixture over shared mixture densities.
Test Set Selection using Active Information Acquisition for Predictive Models
Arising in the context of two rather disparate applications- banking and medical diagnosis, we pose the active information acquisition problem as a constrained optimization problem.
