Search Results for author:
Found 17 papers, 9 papers with code
Cross-Lingual GenQA: Open-Domain Question Answering with Answer Sentence Generation
Recent approaches for question answering systems have achieved impressive performance on English by combining document-level retrieval with answer generation.
Extracting and Inferring Personal Attributes from Dialogue
Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes.
Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text
To support the broad range of real machine errors that can be identified by laypeople, the ten error categories of Scarecrow -- such as redundancy, commonsense errors, and incoherence -- are identified through several rounds of crowd annotation experiments without a predefined ontology.
Go Forth and Prosper: Language Modeling with Ancient Textual History
This method can improve perplexity of pretrained LMs with no updates to the LM's own parameters.
Extracting Summary Knowledge Graphs from Long Documents
Knowledge graphs capture entities and relations from long documents and can facilitate reasoning in many downstream applications.
A Controllable Model of Grounded Response Generation
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses.
Explaining Relationships Between Scientific Documents
We address the task of explaining relationships between two scientific documents using natural language text.
DeFINE: DEep Factorized INput Token Embeddings for Neural Sequence Modeling
For sequence models with large vocabularies, a majority of network parameters lie in the input and output layers.
SemEval-2019 Task 10: Math Question Answering
Systems were evaluated based on the percentage of correctly answered questions.
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
We introduce a new representation language to model precise operation programs corresponding to each math problem that aim to improve both the performance and the interpretability of the learned models.
Text Generation from Knowledge Graphs with Graph Transformers
Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce.
Ranked #6 on
KG-to-Text Generation
on AGENDA
Pyramidal Recurrent Unit for Language Modeling
We introduce the Pyramidal Recurrent Unit (PRU), which enables learning representations in high dimensional space with more generalization power and fewer parameters.
Data-Driven Methods for Solving Algebra Word Problems
We explore contemporary, data-driven techniques for solving math word problems over recent large-scale datasets.
A Theme-Rewriting Approach for Generating Algebra Word Problems
Texts present coherent stories that have a particular theme or overall setting, for example science fiction or western.
Parsing Algebraic Word Problems into Equations
This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees.
