Search Results for author:
Found 11 papers, 7 papers with code
Semantic Parsing for Conversational Question Answering over Knowledge Graphs
In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities).
Memory-Based Semantic Parsing
We present a memory-based model for context-dependent semantic parsing.
Storytelling from Structured Data and Knowledge Graphs : An NLG Perspective
In this tutorial, we wish to cover the foundational, methodological, and system development aspects of translating structured data (such as data in tabular form) and knowledge bases (such as knowledge graphs) into natural language.
Unified Semantic Parsing with Weak Supervision
Semantic parsing over multiple knowledge bases enables a parser to exploit structural similarities of programs across the multiple domains.
Unsupervised Neural Text Simplification
The paper presents a first attempt towards unsupervised neural text simplification that relies only on unlabeled text corpora.
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Text Simplification
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Scalable Micro-planned Generation of Discourse from Structured Data
We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG).
Unsupervised Controllable Text Formalization
We propose a novel framework for controllable natural language transformation.
Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization
For example, while generating descriptions from a table, a human would attend to information at two levels: (i) the fields (macro level) and (ii) the values within the field (micro level).
A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table Summarization
Structured data summarization involves generation of natural language summaries from structured input data.
Story Generation from Sequence of Independent Short Descriptions
Existing Natural Language Generation (NLG) systems are weak AI systems and exhibit limited capabilities when language generation tasks demand higher levels of creativity, originality and brevity.
Topic Modeling Using Distributed Word Embeddings
We propose a new algorithm for topic modeling, Vec2Topic, that identifies the main topics in a corpus using semantic information captured via high-dimensional distributed word embeddings.
