Search Results for author:
Found 14 papers, 2 papers with code
LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback
Recent large language models (LLM) are leveraging human feedback to improve their generation quality.
There's no Data Like Better Data: Using QE Metrics for MT Data Filtering
Quality Estimation (QE), the evaluation of machine translation output without the need of explicit references, has seen big improvements in the last years with the use of neural metrics.
Training and Meta-Evaluating Machine Translation Evaluation Metrics at the Paragraph Level
As research on machine translation moves to translating text beyond the sentence level, it remains unclear how effective automatic evaluation metrics are at scoring longer translations.
Athena 2.0: Discourse and User Modeling in Open Domain Dialogue
Conversational agents are consistently growing in popularity and many people interact with them every day.
Controllable Generation of Dialogue Acts for Dialogue Systems via Few-Shot Response Generation and Ranking
Here we develop a novel few-shot overgenerate-and-rank approach that achieves the controlled generation of DAs.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
Athena 2.0: Contextualized Dialogue Management for an Alexa Prize SocialBot
Athena 2. 0 is an Alexa Prize SocialBot that has been a finalist in the last two Alexa Prize Grand Challenges.
Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLG
Ever since neural models were adopted in data-to-text language generation, they have invariably been reliant on extrinsic components to improve their semantic accuracy, because the models normally do not exhibit the ability to generate text that reliably mentions all of the information provided in the input.
Athena: Constructing Dialogues Dynamically with Discourse Constraints
This report describes Athena, a dialogue system for spoken conversation on popular topics and current events.
ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation
The uptake of deep learning in natural language generation (NLG) led to the release of both small and relatively large parallel corpora for training neural models.
Ranked #2 on
Data-to-Text Generation
on ViGGO
Entertaining and Opinionated but Too Controlling: A Large-Scale User Study of an Open Domain Alexa Prize System
In contrast, search and general Chit-Chat induced coverage problems; here users found it hard to infer what topics SB could understand, with these conversations seen as being too system-driven.
SlugBot: Developing a Computational Model andFramework of a Novel Dialogue Genre
One of the most interesting aspects of the Amazon Alexa Prize competition is that the framing of the competition requires the development of new computational models of dialogue and its structure.
Characterizing Variation in Crowd-Sourced Data for Training Neural Language Generators to Produce Stylistically Varied Outputs
One of the biggest challenges of end-to-end language generation from meaning representations in dialogue systems is making the outputs more natural and varied.
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation
Natural language generation lies at the core of generative dialogue systems and conversational agents.
Ranked #4 on
Data-to-Text Generation
on E2E NLG Challenge
(using extra training data)
