Search Results for author:
Found 26 papers, 11 papers with code
Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code
In this work, we demonstrate that use of a few example conversational prompts can power a conversational agent to generate both natural language and novel code.
GRIM: GRaph-based Interactive narrative visualization for gaMes
\textbf{GRIM}, a prototype \textbf{GR}aph-based \textbf{I}nteractive narrative visualization system for ga\textbf{M}es, generates a rich narrative graph with branching storylines that match a high-level narrative description and constraints provided by the designer.
Investigating Agency of LLMs in Human-AI Collaboration Tasks
While LLMs are being developed to simulate human behavior and serve as human-like agents, little attention has been given to the Agency that these models should possess in order to proactively manage the direction of interaction and collaboration.
Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation
We propose a new grounded keys-to-text generation task: the task is to generate a factual description about an entity given a set of guiding keys, and grounding passages.
Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages
Machine translation has seen rapid progress with the advent of Transformer-based models.
Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization
On several syntactic and semantic probing tasks, we demonstrate the emergent structural information in the role vectors and improved syntactic interpretability in the TPR layer outputs.
GPT Perdetry Test: Generating new meanings for new words
Human innovation in language, such as inventing new words, is a challenge for pretrained language models.
Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge
The ability to generate clarification questions i. e., questions that identify useful missing information in a given context, is important in reducing ambiguity.
Neuro-Symbolic Representations for Video Captioning: A Case for Leveraging Inductive Biases for Vision and Language
In this paper, we propose a new model architecture for learning multi-modal neuro-symbolic representations for video captioning.
Substance over Style: Document-Level Targeted Content Transfer
Existing language models excel at writing from scratch, but many real-world scenarios require rewriting an existing document to fit a set of constraints.
A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks
Learning to align these different instruction sets is challenging because: a) different recipes vary in their order of instructions and use of ingredients; and b) video instructions can be noisy and tend to contain far more information than text instructions.
Contrastive Multi-document Question Generation
In the SL stage, a single-document question generator is trained.
Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts
The readability of a digital text can influence people{'}s ability to learn new things about a range topics from digital resources (e. g., Wikipedia, WebMD).
Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models
A preliminary step towards this goal is to generate a question that captures common concepts of multiple documents.
Controlling the Specificity of Clarification Question Generation
Unlike comprehension-style questions, clarification questions look for some missing information in a given context.
Answer-based Adversarial Training for Generating Clarification Questions
We present an approach for generating clarification questions with the goal of eliciting new information that would make the given textual context more complete.
Multi-Task Neural Models for Translating Between Styles Within and Across Languages
Generating natural language requires conveying content in an appropriate style.
Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information
Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions.
Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer
Style transfer is the task of automatically transforming a piece of text in one particular style into another.
Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task
This paper presents a summary of the first Workshop on Building Linguistically Generalizable Natural Language Processing Systems, and the associated Build It Break It, The Language Edition shared task.
Biomedical Event Extraction using Abstract Meaning Representation
We propose a novel, Abstract Meaning Representation (AMR) based approach to identifying molecular events/interactions in biomedical text.
Parser for Abstract Meaning Representation using Learning to Search
We develop a novel technique to parse English sentences into Abstract Meaning Representation (AMR) using SEARN, a Learning to Search approach, by modeling the concept and the relation learning in a unified framework.
