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Found 10 papers, 8 papers with code
Towards Learning a Generalist Model for Embodied Navigation
We conduct extensive experiments to evaluate the performance and generalizability of our model.
3D Question Answering (3D-QA)
Embodied Question Answering
+3
CLEVA: Chinese Language Models EVAluation Platform
With the continuous emergence of Chinese Large Language Models (LLMs), how to evaluate a model's capabilities has become an increasingly significant issue.
Towards Unifying Multi-Lingual and Cross-Lingual Summarization
In this paper, we aim to unify MLS and CLS into a more general setting, i. e., many-to-many summarization (M2MS), where a single model could process documents in any language and generate their summaries also in any language.
Towards Unifying Reference Expression Generation and Comprehension
Additionally, IRTF could generate pseudo input regions for the REC task to enable a uniform way for sharing the identical representation space across the REC and REG.
A Survey on Cross-Lingual Summarization
Cross-lingual summarization is the task of generating a summary in one language (e. g., English) for the given document(s) in a different language (e. g., Chinese).
Spot the Difference: A Cooperative Object-Referring Game in Non-Perfectly Co-Observable Scene
Visual dialog has witnessed great progress after introducing various vision-oriented goals into the conversation, especially such as GuessWhich and GuessWhat, where the only image is visible by either and both of the questioner and the answerer, respectively.
ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization
We present ClidSum, a benchmark dataset for building cross-lingual summarization systems on dialogue documents.
Knowledge Enhanced Sports Game Summarization
Additionally, we also introduce a knowledge-enhanced summarizer that utilizes both live commentaries and the knowledge to generate sports news.
Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented Guesser
To enhance VD Questioner: 1) we propose a Related entity enhanced Questioner (ReeQ) that generates questions under the guidance of related entities and learns entity-based questioning strategy from human dialogs; 2) we propose an Augmented Guesser (AugG) that is strong and is optimized for the VD setting especially.
Modeling Explicit Concerning States for Reinforcement Learning in Visual Dialogue
In Reinforcement Learning, it is crucial to represent states and assign rewards based on the action-caused transitions of states.
