Search Results for author:
Found 32 papers, 14 papers with code
“It doesn’t look good for a date”: Transforming Critiques into Preferences for Conversational Recommendation Systems
Conversations aimed at determining good recommendations are iterative in nature.
Leveraging Graph Diffusion Models for Network Refinement Tasks
Most real-world networks are noisy and incomplete samples from an unknown target distribution.
OATS: Opinion Aspect Target Sentiment Quadruple Extraction Dataset for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) delves into understanding sentiments specific to distinct elements within a user-generated review.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Knowledge Graph Prompting for Multi-Document Question Answering
Concurrently, the graph traversal agent acts as a local navigator that gathers pertinent context to progressively approach the question and guarantee retrieval quality.
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding
Instruction tuning unlocks the superior capability of Large Language Models (LLM) to interact with humans.
Envisioning the Next-Gen Document Reader
People read digital documents on a daily basis to share, exchange, and understand information in electronic settings.
A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings
In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph.
Graph Learning with Localized Neighborhood Fairness
Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node.
Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs
We present a simple method that avoids both shortcomings: construct the line graph of the network, which includes a node for each interaction, and weigh the edges of this graph based on the difference in time between interactions.
Survey of Aspect-based Sentiment Analysis Datasets
Aspect-based sentiment analysis (ABSA) is a natural language processing problem that requires analyzing user-generated reviews to determine: a) The target entity being reviewed, b) The high-level aspect to which it belongs, and c) The sentiment expressed toward the targets and the aspects.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes
In this paper, we introduce the online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where data points arrive in an arbitrary order and the algorithms are constrained to use a single-pass over the data as well as sub-linear memory.
Exploring Conditional Text Generation for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is an NLP task that entails processing user-generated reviews to determine (i) the target being evaluated, (ii) the aspect category to which it belongs, and (iii) the sentiment expressed towards the target and aspect pair.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
"It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systems
Conversations aimed at determining good recommendations are iterative in nature.
StreamHover: Livestream Transcript Summarization and Annotation
With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into this wealth of knowledge.
Syntopical Graphs for Computational Argumentation Tasks
Approaches to computational argumentation tasks such as stance detection and aspect detection have largely focused on the text of independent claims, losing out on potentially valuable context provided by the rest of the collection.
Developing a Conversational Recommendation System for Navigating Limited Options
We have developed a conversational recommendation system designed to help users navigate through a set of limited options to find the best choice.
Edge: Enriching Knowledge Graph Embeddings with External Text
Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods.
Learning to Emphasize: Dataset and Shared Task Models for Selecting Emphasis in Presentation Slides
We evaluate a range of state-of-the-art models on this novel dataset by organizing a shared task and inviting multiple researchers to model emphasis in this new domain.
Learning Contextualized Knowledge Graph Structures for Commonsense Reasoning
Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense) question answering and natural language inference.
Learning Contextualized Knowledge Structures for Commonsense Reasoning
Recently, knowledge graph (KG) augmented models have achieved noteworthy success on various commonsense reasoning tasks.
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
Knowledge graphs (KGs) have helped neural models improve performance on various knowledge-intensive tasks, like question answering and item recommendation.
Graph Neural Networks with Heterophily
Graph Neural Networks (GNNs) have proven to be useful for many different practical applications.
SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media
In this paper, we present the main findings and compare the results of SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media.
ISA: An Intelligent Shopping Assistant
Despite the growth of e-commerce, brick-and-mortar stores are still the preferred destinations for many people.
Let Me Choose: From Verbal Context to Font Selection
In this paper, we aim to learn associations between visual attributes of fonts and the verbal context of the texts they are typically applied to.
Towards Summarization for Social Media - Results of the TL;DR Challenge
In this paper, we report on the results of the TL;DR challenge, discussing an extensive manual evaluation of the expected properties of a good summary based on analyzing the comments provided by human annotators.
Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions
In visual communication, text emphasis is used to increase the comprehension of written text to convey the author{'}s intent.
Towards Open Intent Discovery for Conversational Text
Existing research for intent discovery model it as a classification task with a predefined set of known categories.
Supervised Transfer Learning for Product Information Question Answering
Popular e-commerce websites such as Amazon offer community question answering systems for users to pose product related questions and experienced customers may provide answers voluntarily.
Task Proposal: The TL;DR Challenge
The TL;DR challenge fosters research in abstractive summarization of informal text, the largest and fastest-growing source of textual data on the web, which has been overlooked by summarization research so far.
A Simple End-to-End Question Answering Model for Product Information
When evaluating a potential product purchase, customers may have many questions in mind.
