Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension (D18-1)
Copied to clipboard
| Challenge: | Sequence encoders are crucial components in many neural architectures for learning to read and comprehend. |
| Approach: | They propose a compositional encoder that explicitly models across multiple granularities using a new dilated composition mechanism. |
| Outcome: | The proposed encoder is fast and expressive, and can model across multiple granularities. |
Similar Papers
Disentangled Sequence to Sequence Learning for Compositional Generalization (2022.acl-long)
Copied to clipboard
| Challenge: | Existing models struggle to generalize to unseen compositions of seen components . a new approach allows for disentangled representations and better generalization . |
| Approach: | They propose an extension to sequence-to-sequence models which encourage disentanglement by re-encoding source input. |
| Outcome: | The proposed extension delivers better generalization and more disentangled representations . human expressions can be understood by combining known atomic components . |
Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies show that sequence-to-sequence (seq2sequ) models struggle with compositional generalization (CG) a crucial property of human language learning is its compositional globalization (GC), the algebraic ability to understand and produce a potentially infinite number of novel combinations from known components. |
| Approach: | They propose a sequence-to-sequence (seq2sequ) extension which learns to compose representations of different encoder layers dynamically for different tasks. |
| Outcome: | The proposed model achieves competitive results on two comprehensive and realistic benchmarks, which empirically demonstrates the effectiveness of the proposed model. |
M3: A Multi-View Fusion and Multi-Decoding Network for Multi-Document Reading Comprehension (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for multi-document reading comprehension cannot make full of the advantages of both approaches. |
| Approach: | They propose a multi-view fusion and multi-decoding method that integrates multiple documents for answering questions. |
| Outcome: | The proposed method improves on two mainstream multi-document reading comprehension datasets. |
Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to machine reading comprehension (MRC) on long texts typically chunk text into equally-spaced segments without considering information from other segments. |
| Approach: | They propose to let a model learn to chunk in a more flexible way via reinforcement learning. |
| Outcome: | The proposed model extracts a text span from document and query as answer . previous models can only take a fixed-length (e.g., 512) text as input . |
Continuous Decomposition of Granularity for Neural Paraphrase Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Prior work has shown that decomposing sentences at different levels of granularity has improved paragraph generation. |
| Approach: | They propose a model for continuous decomposing granularity for neural paraphrase generation that incorporates granules into attention. |
| Outcome: | The proposed model outperforms baseline models on Quora question pairs and Twitter URLs on two benchmarks. |
Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding (2020.acl-main)
Copied to clipboard
| Challenge: | Document-level event extraction requires a view of a larger context to determine which spans of text correspond to event role fillers. |
| Approach: | They propose a multi-granularity reader to dynamically aggregate information captured by neural representations learned at different levels of granularities. |
| Outcome: | The proposed model performs substantially better than previous models on the MUC-4 event extraction dataset. |
Improving Compositional Generalization in Classification Tasks via Structure Annotations (2021.acl-short)
Copied to clipboard
| 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. |
Real-World Compositional Generalization with Disentangled Sequence-to-Sequence Learning (2023.findings-acl)
Copied to clipboard
| 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. |
Inducing Transformer’s Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing neural models lack systematic compositionality in learning symbolic structures . existing models lack this ability in learning symbols, despite being able to understand complex structures. |
| Approach: | They propose to use auxiliary sequence prediction tasks to train a Transformer model to understand compositional symbolic structures of input data. |
| Outcome: | The proposed model improves on the SCAN compositionality challenge, with only 418 (5%) training instances, and achieves 97.8% accuracy on the MCD1 split. |
MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding (2022.emnlp-main)
Copied to clipboard
Zilong Wang, Jiuxiang Gu, Chris Tensmeyer, Nikolaos Barmpalios, Ani Nenkova, Tong Sun, Jingbo Shang, Vlad Morariu
| Challenge: | Existing methods learn features from word-level or region-level but fail to consider both simultaneously. |
| Approach: | They propose a multi-modal multi-granular pre-training framework that encodes page-level, region-level and word-level information at the same time. |
| Outcome: | The proposed model learns features from word-level and region-level but fails to consider both simultaneously. |