2 |
What happens if you treat ordinal ratings as interval data? Human evaluations in {NLP} are even more under-powered than you think
|
|
|
|
BASE
|
|
Show details
|
|
4 |
OTTers: One-turn Topic Transitions for Open-Domain Dialogue ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Detection in Conversational AI ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
What happens if you treat ordinal ratings as interval data? Human evaluations in NLP are even more under-powered than you think ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definition
|
|
|
|
BASE
|
|
Show details
|
|
10 |
SLURP: A Spoken Language Understanding Resource Package ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
SLURP: A Spoken Language Understanding Resource Package ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
SLURP: A Spoken Language Understanding Resource Package ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
Conversational Assistants and Gender Stereotypes: Public Perceptions and Desiderata for Voice Personas ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
RankME: Reliable Human Ratings for Natural Language Generation ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Better Conversations by Modeling,Filtering,and Optimizing for Coherence and Diversity ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
The E2E Dataset: New Challenges For End-to-End Generation ...
|
|
|
|
BASE
|
|
Show details
|
|
20 |
The REAL corpus
|
|
|
|
Abstract:
Our interest is in people’s capacity to efficiently and effectively describe geographic objects in urban scenes. The broader ambition is to develop spatial models capable of equivalent functionality able to construct such referring expressions. To that end we present a newly crowd-sourced data set of natural language references to objects anchored in complex urban scenes (In short: The REAL Corpus – Referring Expressions Anchored Language). The REAL corpus contains a collection of images of real-world urban scenes together with verbal descriptions of target objects generated by humans, paired with data on how successful other people were able to identify the same object based on these descriptions. In total, the corpus contains 32 images with on average 27 descriptions per image and 3 verifications for each description. In addition, the corpus is annotated with a variety of linguistically motivated features. The paper highlights issues posed by collecting data using crowd-sourcing with an unrestricted input format, as well as using real-world urban scenes.
|
|
URL: http://researchrepository.napier.ac.uk/Output/2658045
|
|
BASE
|
|
Hide details
|
|
|
|