Grounded Graph Decoding improves Compositional Generalization in Question Answering (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Current compositional generalization models lose syntax context when learning a flat input . a new method to improve compositional globalization is proposed to ground structured predictions with an attention mechanism. |
| Approach: | They propose a method to ground structured predictions by a structure-based attention mechanism. |
| Outcome: | The proposed method performs competitively on the Compositional Freebase Questions dataset. |
Similar Papers
Compositional Generalization with Grounded Language Models (2024.findings-acl)
Copied to clipboard
| 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. |
Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering (2021.tacl-1)
Copied to clipboard
| Challenge: | Neural networks fail to generalize to out-of-distribution examples that contain new compositions. |
| Approach: | They propose a model that computes a representation and denotation for all question spans in a bottom-up, compositional manner using a CKY-style parser. |
| Outcome: | The proposed model achieves 96.1% accuracy on a challenging dataset compared to baseline models on . previous models failed to generalize to out-of-distribution examples . |
Compositional Networks Enable Systematic Generalization for Grounded Language Understanding (2021.findings-emnlp)
Copied to clipboard
| 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. |
Grammar-based Decoding for Improved Compositional Generalization in Semantic Parsing (2023.findings-acl)
Copied to clipboard
| Challenge: | Sequence-to-sequence (seq2sequ) models have been successful in semantic parsing tasks but struggle on out-of-distribution data. |
| Approach: | They propose to use a large-scale dialogue dataset to evaluate compositional generalization of semantic parsing. |
| Outcome: | The proposed model outperforms BART- and T5-based models on the SMCalflow-CS dataset on the zero-shot learning task. |
Neural Compositional Denotational Semantics for Question Answering (D18-1)
Copied to clipboard
| Challenge: | a new model for compositional questions is needed to answer multi-step reasoning . the model is inspired by formal approaches to compositional semantics . |
| Approach: | They propose an end-to-end differentiable model for interpreting compositional questions . they build a latent tree of interpretable expressions over a sentence . |
| Outcome: | The proposed model outperforms RNN encoders when test questions are longer than training questions. |
The Mystery of Compositional Generalization in Graph-based Generative Commonsense Reasoning (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have found that LLMs are limited in scenarios that require generalization abilities, such as out-of-domain tasks. |
| Approach: | They propose a Compositional Generalization Challenge for Graph-based Commonsense Reasoning that requires models to generate a natural sentence based on given concepts and a reasoning graph. |
| Outcome: | The proposed framework is based on seven well-known LLMs and shows that they struggle in compositional generalization. |
Compositional Generalization in Grounded Language Learning via Induced Model Sparsity (2022.naacl-srw)
Copied to clipboard
| Challenge: | induced model sparsity can help achieve compositional generalization and sample efficiency in grounded language learning problems. |
| Approach: | They propose a model that encourages sparse correlations between words and attributes to find a goal in a language-conditioned navigation problem with disentangled observations. |
| Outcome: | The proposed agent maintains high performance even when learning from a handful of demonstrations. |
Generating Demonstrations for In-Context Compositional Generalization in Grounded Language Learning (2024.emnlp-main)
Copied to clipboard
| Challenge: | In-Context-learning and few-shot prompting are viable methods for compositional output generation but they are sensitive to the choice of support examples. |
| Approach: | They propose a method which generates supports and targets current state of the world and then uses them in-context-learning to solve a query. |
| Outcome: | The proposed agent improves performance on a previously unsolved compositional generalization test without loss of performance in other areas. |
When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent benchmarks like ReaSCAN use navigation tasks grounded in a grid world to assess whether neural models exhibit compositional behaviour. |
| Approach: | They propose a transformer-based model that outperforms specialized architectures on ReaSCAN and a modified version of gSCAN to test their performance. |
| Outcome: | The proposed model outperforms specialized architectures on ReaSCAN and gSCAN on a grid world and can generalize to deeper input structures. |
Structural generalization in COGS: Supertagging is (almost) all you need (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have shown that neural networks fail to generalize on out-of-distribution examples. |
| Approach: | They extend a neural graph-based parsing framework to address compositional generalization limitations . they introduce a supertagging step with valency constraints and reduce the graph prediction problem . |
| Outcome: | The proposed approach improves results on COGS datasets that require structural generalization. |