Search Results for author:
Found 15 papers, 4 papers with code
Constraint-based Multi-hop Question Answering with Knowledge Graph
The objective of a Question-Answering system over Knowledge Graph (KGQA) is to respond to natural language queries presented over the KG.
ICM : Intent and Conversational Mining from Conversation Logs
Building conversation agents requires a large amount of manual effort in creating training data for intents / entities as well as mapping out extensive conversation flows.
Unknown Intent Detection Using Multi-Objective Optimization on Deep Learning Classifiers
This task of separating the unknown intent samples from known intents one is challenging as the unknown user intent can range from intents similar to the predefined intents to something completely different.
Document Retrieval and Claim Verification to Mitigate COVID-19 Misinformation
The current vogue is to employ manual fact-checkers to verify claims to combat this avalanche of misinformation.
COFAR: Commonsense and Factual Reasoning in Image Search
To enable both commonsense and factual reasoning in the image search, we present a unified framework, namely Knowledge Retrieval-Augmented Multimodal Transformer (KRAMT), that treats the named visual entities in an image as a gateway to encyclopedic knowledge and leverages them along with natural language query to ground relevant knowledge.
Hollywood Identity Bias Dataset: A Context Oriented Bias Analysis of Movie Dialogues
Since AI solutions are data intensive and there exists no domain specific data to address the problem of biases in scripts, we introduce a new dataset of movie scripts that are annotated for identity bias.
An Inference Approach To Question Answering Over Knowledge Graphs
The query conversion models and direct models both require specific training data pertaining to the domain of the knowledge graph.
DESYR: Definition and Syntactic Representation Based Claim Detection on the Web
To demarcate between a claim and a non-claim is arduous for both humans and machines, owing to latent linguistic variance between the two and the inadequacy of extensive definition-based formalization.
Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights
The growing quantity and complexity of data pose challenges for humans to consume information and respond in a timely manner.
Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets
In this work, we present ENDEMIC, a novel early detection model which leverages exogenous and endogenous signals related to tweets, while learning on limited labeled data.
Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection
As the COVID-19 pandemic sweeps across the world, it has been accompanied by a tsunami of fake news and misinformation on social media.
Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy
In this paper, we propose a hybrid technique for semantic question matching.
Causal BERT : Language models for causality detection between events expressed in text
Therefore, as expected these methods are more geared towards handling explicit causal relationships leading to limited coverage for implicit relationships and are hard to generalize.
Intent Mining from past conversations for conversational agent
In this paper, we present an intent discovery framework that involves 4 primary steps: Extraction of textual utterances from a conversation using a pre-trained domain agnostic Dialog Act Classifier (Data Extraction), automatic clustering of similar user utterances (Clustering), manual annotation of clusters with an intent label (Labeling) and propagation of intent labels to the utterances from the previous step, which are not mapped to any cluster (Label Propagation); to generate intent training data from raw conversations.
