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Found 9 papers, 7 papers with code
Automatic Evaluation vs. User Preference in Neural Textual QuestionAnswering over COVID-19 Scientific Literature
In this paper we present a quantitative and qualitative analysis of the system.
Grounding Spatial Relations in Text-Only Language Models
This paper shows that text-only Language Models (LM) can learn to ground spatial relations like "left of" or "below" if they are provided with explicit location information of objects and they are properly trained to leverage those locations.
Improving Explicit Spatial Relationships in Text-to-Image Generation through an Automatically Derived Dataset
We hypothesize that this is because explicit spatial relations rarely appear in the image captions used to train these models.
Unsupervised Domain Adaption for Neural Information Retrieval
We focus on the BEIR benchmark, which includes test datasets from several domains with no training data, and explore two scenarios: zero-shot, where the supervised system is trained in a large out-of-domain dataset (MS-MARCO); and unsupervised domain adaptation, where, in addition to MS-MARCO, the system is fine-tuned in synthetic data from the target domain.
Do Multilingual Language Models Think Better in English?
In this work, we introduce a new approach called self-translate, which overcomes the need of an external translation system by leveraging the few-shot translation capabilities of multilingual language models.
Common Sense Reasoning
Cross-Lingual Natural Language Inference
+6
Image Captioning for Effective Use of Language Models in Knowledge-Based Visual Question Answering
Our results on a visual question answering task which requires external knowledge (OK-VQA) show that our text-only model outperforms pretrained multimodal (image-text) models of comparable number of parameters.
Inferring spatial relations from textual descriptions of images
Previous work did not use the caption text information, but a manually provided relation holding between the subject and the object.
Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning
The interaction of conversational systems with users poses an exciting opportunity for improving them after deployment, but little evidence has been provided of its feasibility.
Evaluating Multimodal Representations on Visual Semantic Textual Similarity
In the case of textual representations, inference tasks such as Textual Entailment and Semantic Textual Similarity have been often used to benchmark the quality of textual representations.
