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Found 18 papers, 2 papers with code
The Impact of Answers in Referential Visual Dialog
Here we present work in progress in the study of the impact of different answering models in human generated questions in GuessWhat?!.
Region under Discussion for visual dialog
Visual Dialog is assumed to require the dialog history to generate correct responses during a dialog.
Visually Grounded Follow-up Questions: a Dataset of Spatial Questions Which Require Dialogue History
We call the question that restricts the context: trigger, and we call the spatial question that requires the trigger question to be answered: zoomer.
They Are Not All Alike: Answering Different Spatial Questions Requires Different Grounding Strategies
By analyzing LXMERT errors and its attention mechanisms, we find that our classification helps to gain a better understanding of the skills required to answer different spatial questions.
What kinds of errors do reference resolution models make and what can we learn from them?
Referring resolution is the task of identifying the referent of a natural language expression, for example “the woman behind the other woman getting a massage”.
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data.
Selectively Answering Visual Questions
We perform the first in-depth analysis of calibration methods and metrics for VQA with in-context learning LMMs.
A methodology to characterize bias and harmful stereotypes in natural language processing in Latin America
Our methodology is based on the following principles: * focus on the linguistic manifestations of discrimination on word embeddings and language models, not on the mathematical properties of the models * reduce the technical barrier for discrimination experts%, be it social scientists, domain experts or other * characterize through a qualitative exploratory process in addition to a metric-based approach * address mitigation as part of the training process, not as an afterthought
A recipe for annotating grounded clarifications
In this paper, we argue that dialogue clarification mechanisms make explicit the process of interpreting the communicative intents of the speaker's utterances by grounding them in the various modalities in which the dialogue is situated.
Grounding as a Collaborative Process
Collaborative grounding is a fundamental aspect of human-human dialog which allows people to negotiate meaning.
Effective questions in referential visual dialogue
Task success is the standard metric used to evaluate these systems.
On the role of effective and referring questions in GuessWhat?!
Regarding our first metric, we find that successful dialogues do not have a higher percentage of effective questions for most models.
Modeling Student Response Times: Towards Efficient One-on-one Tutoring Dialogues
Knowing how long it would normally take a student to respond to different types of questions could help tutors optimize their own time while answering multiple dialogues concurrently, as well as deciding when to prompt a student again.
