Papers by Peter Shaw
Self-Attention with Relative Position Representations (N18-2)
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| Challenge: | Recent approaches to sequence to sequence learning leverage recurrence, convolution, attention or combination of recurrent and convolutional neural networks. |
| Approach: | They propose an approach that extends the self-attention mechanism to consider representations of relative positions, or distances between sequence elements. |
| Outcome: | The proposed approach yields 1.3 BLEU and 0.3 BLUE on translation tasks . it is based on a relation-aware self-attention mechanism that can generalize to arbitrary graph-labeled inputs. |
Generate-and-Retrieve: Use Your Predictions to Improve Retrieval for Semantic Parsing (2022.coling-1)
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Yury Zemlyanskiy, Michiel de Jong, Joshua Ainslie, Panupong Pasupat, Peter Shaw, Linlu Qiu, Sumit Sanghai, Fei Sha
| Challenge: | Existing retrieval techniques for semantic parsing use similarity of query and exemplar inputs . Existing work suggests that appending training samples to training samples improves performance . |
| Approach: | They propose a retrieval procedure that retrieves exemplars for which outputs are similar . existing retrieval techniques are based on similarity of query and exemplar inputs . |
| Outcome: | Existing retrieval techniques rely on similarity of query and exemplar inputs . they retrieve exemplars with similar outputs and generate a final prediction . |
Systematic Generalization on gSCAN: What is Nearly Solved and What is Next? (2021.emnlp-main)
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| Challenge: | a general-purpose Transformer-based model with crossmodal attention solves most of the systematic generalization problems . current models are data inefficient given the narrow scope of commands in gSCAN . |
| Approach: | They propose to use a Transformer-based model with cross-modal attention to solve gSCAN . they propose to generate data to incorporate relations between objects in the visual environment . |
| Outcome: | The proposed model outperforms specialized approaches on most splits, and is data inefficient given the narrow scope of commands. |
Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing (2020.acl-main)
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| Challenge: | Existing evaluation datasets such as Spider are used to support cross-database semantic parsing . XSP systems that map natural language utterances to SQL queries are evaluated on databases unseen during training. |
| Approach: | They propose a setup that uses eight well-studied datasets to evaluate cross-database semantic parsing systems. |
| Outcome: | The proposed system performs well on spider, but struggles to generalize to the repurposed set. |
QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations (2023.acl-long)
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| Challenge: | People express information needs with multiple preferences or constraints . modern retrieval systems struggle on such queries, a study finds . |
| Approach: | They construct a dataset of 3357 queries that map to a set of Wikipedia entities . they use crowd-sourced data to match constraints with evidence in documents . |
| Outcome: | The proposed dataset challenges models to match constraints mentioned in queries with evidence in documents and correctly perform various set operations. |
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing (2022.emnlp-main)
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Linlu Qiu, Peter Shaw, Panupong Pasupat, Tianze Shi, Jonathan Herzig, Emily Pitler, Fei Sha, Kristina Toutanova
| Challenge: | Pre-trained language models struggle on out-of-distribution compositional generalization . recent work shows considerable improvements on many NLP tasks from model scaling . |
| Approach: | They evaluate encoder-decoder models up to 11B parameters and decoder-only models up 540B parameters . they compare scaling curves for fine-tuning, prompt tuning, and in-context learning methods . |
| Outcome: | The proposed scaling methods improve compositional generalization on many tasks . fine-tuning generally has flat or negative scaling curves on out-of-distribution compositional . larger models are better at modeling the syntax of the output space, the study finds . |
Answering Conversational Questions on Structured Data without Logical Forms (D19-1)
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| Challenge: | Existing approaches to answering sequential questions based on structured objects do not use a logical form as an intermediate representation. |
| Approach: | They propose a novel approach to answering sequential questions based on structured objects without using a logical form as an intermediate representation. |
| Outcome: | The proposed approach is competitively tested on the Sequential Question Answering (SQA) task. |
Graph-Based Decoding for Task Oriented Semantic Parsing (2021.findings-emnlp)
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| Challenge: | Existing paradigms for semantic parsing are sequence-to-sequence and AMR parsers. |
| Approach: | They propose to formulate parsing as a sequence-to-sequence task using graph-based decoding techniques developed for syntactic parsers. |
| Outcome: | The proposed approach is competitive with sequence decoders on the standard setting and offers significant improvements in data efficiency and data availability. |
Visually Grounded Concept Composition (2021.findings-emnlp)
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| Challenge: | Existing approaches to visual grounding do not explicitly model compositional structures of text expressions. |
| Approach: | They propose a concept-relation Graph and a composition neural network to combine CRGs . they propose to align CRG-based concepts with images to learn visually grounded concepts . |
| Outcome: | The proposed model can model grounded concepts forming at sentence level and word level. |
Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both? (2021.acl-long)
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| Challenge: | Existing approaches to semantic parsing only evaluated on synthetic datasets that are not representative of natural language variation. |
| Approach: | They propose a semantic parsing approach that handles both natural language variation and compositional generalization. |
| Outcome: | The proposed model outperforms existing models across compositional generalization challenges on non-synthetic datasets while being competitive with the state-of-the-art on standard evaluations. |
Generating Logical Forms from Graph Representations of Text and Entities (P19-1)
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| Challenge: | Recent approaches to semantic parsing have cast it as a sequence-to-sequence task, with strong results. |
| Approach: | They propose a Graph Neural Network architecture to incorporate information about relevant entities and their relations during parsing. |
| Outcome: | The proposed approach outperforms the state-of-the-art in several tasks without pre-training and outperformed existing approaches when combined with BERT pre-trainment. |
Improving Compositional Generalization with Latent Structure and Data Augmentation (2022.naacl-main)
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| Challenge: | Generic unstructured neural networks struggle on out-of-distribution compositional generalization. |
| Approach: | They propose a method to recombinate examples from a model called Compositional Structure Learner and add them to a pre-trained sequence-to-sequence model. |
| Outcome: | The proposed model is even stronger than a T5-CSL ensemble on two real world compositional generalization tasks. |