Papers with learning agents
TASER: Table Agents for Schema-guided Extraction and Recommendation (2026.eacl-industry)
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| Challenge: | Real-world financial filings report critical information about an entity’s investment holdings, but they are often buried in messy, multi-page, fragmented tables that are difficult to parse. |
| Approach: | They propose to train a system that converts highly unstructured, multi-page, heterogeneous tables into normalized, schema-conforming outputs. |
| Outcome: | The proposed system outperforms vision-based table detection models by 10.1% and can generate more useful recommendations by 10%. |
Dialogue in the Wild: Learning from a Deployed Role-Playing Game with Humans and Bots (2021.findings-acl)
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| Challenge: | prevailing paradigm in natural language processing research is to build a fixed dataset and freeze it, without any ability for the model to interact with humans using language at training time at all. |
| Approach: | They build and deploy a role-playing game where players converse with learning agents situated in an open-domain fantasy world. |
| Outcome: | The proposed game enables human players to learn from human conversations and improves on their models. |
The Context-Dependent Additive Recurrent Neural Net (N18-1)
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| Challenge: | Contextual sequence mapping is one of the fundamental problems in Natural Language Processing (NLP). |
| Approach: | They propose a new family of Recurrent Neural Networks that address contextual sequence mapping . they propose to use contextual signals to control the flow of information . |
| Outcome: | The proposed architecture outperforms existing methods on dialog problem and language model . the proposed architectures are based on a novel family of recurrent neural networks . |
Distinguishing fair from unfair compositional generalization tasks (2025.findings-emnlp)
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| Challenge: | Compositional generalization benchmarks assess learning agents' ability to combine familiar concepts in novel ways. |
| Approach: | They propose to use compositional generalization benchmarks to assess learning agents' ability to combine familiar concepts in novel ways. |
| Outcome: | The proposed tasks are easy and hard, but no present-day models get any traction. |