| Challenge: | a new method for compositional action recognition is proposed to address the problem of zero-shot learning. |
| Approach: | They propose a method to generalize compositional action recognition models to new verbs and nouns . they use knowledge graphs to extract disentangled feature representations for verbs, noun and type constraint . |
| Outcome: | The proposed approach improves generalization ability of the compositional action recognition model to novel verbs and nouns that are unseen during training time. |
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Scalable Construction and Reasoning of Massive Knowledge Bases (N18-6)
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| Challenge: | Existing knowledge mining systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. |
| Approach: | They introduce how to extract structured facts from text corpora to construct knowledge bases. |
| Outcome: | The proposed methods are weakly-supervised and domain-independent for knowledge base construction across various domains. |
Real-World Compositional Generalization with Disentangled Sequence-to-Sequence Learning (2023.findings-acl)
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| Challenge: | Existing approaches to compositional generalization have been designed with semantic parsing in mind. |
| Approach: | They propose a disentangled sequence-to-sequence model which encourages more disentanglement and improves its compute and memory efficiency. |
| Outcome: | The proposed model improves generalization performance across existing tasks and datasets and a new machine translation benchmark. |
Building Hierarchically Disentangled Language Models for Text Generation with Named Entities (2020.coling-main)
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| Challenge: | Named entities pose a unique challenge to traditional methods of language modeling. |
| Approach: | They propose a Hierarchically Disentangled Model for named entities in cooking recipes using a dataset from several publicly available online sources. |
| Outcome: | The proposed model is based on 158,473 cooking recipes from public sources. |
Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality (2023.emnlp-main)
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| Challenge: | Recent studies have highlighted severe limitations of contrastive learning models in their ability to perform compositional reasoning over objects, attributes, and relations. |
| Approach: | They propose a graph decomposition framework and negative mining techniques to improve attribute binding and relation understanding of scene graphs. |
| Outcome: | The proposed approach improves attribute binding, relation understanding, generalization, and productivity on multiple benchmarks. |
Deep Dungeons and Dragons: Learning Character-Action Interactions from Role-Playing Game Transcripts (N18-2)
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| Challenge: | a novel approach to understanding narratives involves modelling the interaction between characters and actions . we propose role-playing games as a testbed for inferring interactions between characters in narratives . |
| Approach: | They propose role-playing games as a testbed for learning latent ties between characters and actions . they propose to combine character and action descriptions from online discussion forums . |
| Outcome: | The proposed model can capture interactions between characters and actions in narratives . it can predict actions better when character attributes are taken into account . |
Improving Compositional Generalization in Classification Tasks via Structure Annotations (2021.acl-short)
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| Challenge: | Compositional generalization is the ability to generalize systematically to a new data distribution by combining known components. |
| Approach: | They propose to convert a natural language sequence-to-sequence dataset into a classification dataset that requires compositional generalization. |
| Outcome: | The proposed model can generalize compositionally by providing hints on the structure of the input. |
Action Verb Corpus (L18-1)
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| Challenge: | a corpus of 390 simple actions is based on multimodal data of 12 humans . the dataset is annotated with orthographic transcriptions of utterances and part-of-speech tags . |
| Approach: | They present a multimodal corpus of 12 humans performing 390 simple actions . they also propose an algorithm for segmenting words into utterances and aligning visual information and speech . |
| Outcome: | The presented dataset includes 390 simple actions performed by 12 humans . it includes transcriptions of utterances, part-of-speech tags, lemmata, and hand touches . |
Compositional Generalization with Grounded Language Models (2024.findings-acl)
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| Challenge: | Existing methods for combining language models with knowledge graphs struggle with generalization to sequences of unseen lengths and novel combinations of seen base components. |
| Approach: | They propose a procedure for generating natural language questions paired with knowledge graphs that targets different aspects of compositionality and avoids grounding models in information already encoded in their weights. |
| Outcome: | The proposed method fails to generalize to unseen lengths and to novel combinations of seen base components. |
Compositional Networks Enable Systematic Generalization for Grounded Language Understanding (2021.findings-emnlp)
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| Challenge: | a recent study shows that deep networks can mimic some human language abilities when presented with novel sentences . a general-purpose mechanism that enables agents to generalize their language understanding to compositional domains is critical to building safe and fair robots, says a new study. |
| Approach: | They build a general-purpose mechanism that enables agents to generalize their language understanding to compositional domains. |
| Outcome: | a new network generalizes its language understanding to compositional domains while generalizing its knowledge when prior work does not. |
Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation (2023.acl-long)
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| Challenge: | Existing controllable dialogue generation models focus on single attribute and lack generalization capability to out-of-distribution multiple attribute combinations. |
| Approach: | They propose a compositional generalization model that learns from seen attributes and generalizes to unseen combinations. |
| Outcome: | The proposed model can learn from seen attribute values and generalize to unseen combinations. |