Papers by Luheng He
Crowdsourcing Question-Answer Meaning Representations (N18-2)
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| Challenge: | Existing datasets for predicate-argument relationships are lacking highly skilled and trained annotators. |
| Approach: | They propose a crowdsourcing scheme to generate question-answer pairs that represent predicate-argument relationships in sentences as a set of question-announcer pairs. |
| Outcome: | The proposed model covers the vast majority of predicate-argument relationships in existing datasets along with many previously under-resourced ones, including implicit arguments and relations. |
TableFormer: Robust Transformer Modeling for Table-Text Encoding (2022.acl-long)
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| Challenge: | Existing tables models require linearization of the table structure, where row or column order is encoded as an unwanted bias. |
| Approach: | They propose a robust and structurally aware table-text encoding architecture TableFormer where tabular structural biases are incorporated completely through learnable attention biase. |
| Outcome: | The proposed architecture outperforms strong baselines on SQA, WTQ and TabFact table reasoning datasets and achieves state-of-the-art performance on SQ. |
A general framework for information extraction using dynamic span graphs (N19-1)
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| Challenge: | Existing frameworks for information extraction use a pipeline approach to identify entities and then use the detected entity spans for relation extraction and coreference resolution. |
| Approach: | They propose a framework for several information extraction tasks that share span representations using dynamically constructed span graphs. |
| Outcome: | The proposed framework significantly outperforms state-of-the-art on multiple information extraction tasks across multiple datasets reflecting different domains. |
PAWS: Paraphrase Adversaries from Word Scrambling (N19-1)
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| Challenge: | Existing paraphrase identification datasets lack sentence pairs with high word overlap without being paraphrases. |
| Approach: | They propose a workflow for generating pairs of sentences with high word overlap . they use controlled word swapping and back translation followed by fluency and paraphrase judgments . |
| Outcome: | The proposed dataset has 108,463 well-formed paraphrase and non-paraphrase pairs with high lexical overlap. |
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension (D19-1)
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| Challenge: | End-to-end reading comprehension models have been successful at extracting text answers, but there are still problems with generalizing them to abstractive numerical reasoning. |
| Approach: | They propose to augment a BERT-based reading comprehension model with a set of executable ‘programs’ which encompass simple arithmetic as well as extraction. |
| Outcome: | The proposed model can perform 33% absolute improvement on the DROP dataset, with very few training examples. |
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. |
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction (D18-1)
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| Challenge: | Existing relation extraction systems are designed for within-sentence relations, but extracting information from scientific articles requires relations across sentences. |
| Approach: | They propose a multi-task setup for identifying entities, relations, and coreference clusters in scientific articles . they develop a unified framework called SciIE with shared span representations to solve this problem . |
| Outcome: | The proposed model outperforms existing models without domain-specific features in scientific information extraction. |
TIMEDIAL: Temporal Commonsense Reasoning in Dialog (2021.acl-long)
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| Challenge: | Existing studies on pre-trained language models for dialog reasoning fail to understand context correctly. |
| Approach: | They propose to use a crowd-sourced English task and a time-based task to test models' temporal reasoning abilities in dialogs. |
| Outcome: | The proposed task and crowd-sourced English challenge set show that even the best performing models struggle on this task compared to humans. |
Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements (2020.emnlp-main)
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| Challenge: | Existing tools for examining and fixing missing captions are lacking in mobile UIs. |
| Approach: | They propose a task for automatically generating language descriptions for UI elements from multimodal input including both the image and structural representations of user interfaces. |
| Outcome: | The proposed task can generate captions from image and structural representations of UI elements. |
Large-Scale QA-SRL Parsing (P18-1)
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| Challenge: | a crowd-sourced approach to learning semantic parsers to predict predicateargument structures is open to many researchers. |
| Approach: | They propose a large-scale corpus of Question-Answer driven Semantic Role Labeling annotations . they also propose QA-SRL Bank 2.0, a crowd-sourcing scheme that can be used to train high quality parsers . |
| Outcome: | The proposed QA-SRL parser can generate high-quality questions at low cost and is intuitive to non-experts. |
Few-shot Intent Classification and Slot Filling with Retrieved Examples (2021.naacl-main)
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| Challenge: | Existing methods for few-shot learning are based on labeled examples, but they are non-trivial . few-sshot learning is challenging due to the imbalance in the amount of data between the source and target domains. |
| Approach: | They propose retrieval-based methods for intent classification and slot filling tasks . they use a batch-softmax objective to learn similar contextualized representations for spans . |
| Outcome: | The proposed method outperforms previous systems on the CLINC and SNIPS benchmarks. |
QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining (2021.acl-short)
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| Challenge: | Existing methods to predict slots and their values do not encode enough semantic information, limiting the models’ zero-shot capability. |
| Approach: | They propose a QA-driven slot filling model which extracts slot-filler spans from utterances with a span-based QA model. |
| Outcome: | The proposed model outperforms baselines by over 5% on the SNIPS benchmark. |
Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling (P18-2)
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| Challenge: | Recent models that use gold predicates only use a single predicate at a time. |
| Approach: | They propose an end-to-end approach for jointly predicting all predicates, arguments spans, and the relations between them. |
| Outcome: | The proposed model can model overlapping spans across different predicates in the same output structure without gold predicate predications. |
Higher-Order Coreference Resolution with Coarse-to-Fine Inference (N18-2)
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| Challenge: | a new approach to coreference resolution uses a span-ranking architecture as an attention mechanism to iteratively refine span representations. |
| Approach: | They propose a fully-differentiable approximation to higher-order inference for coreference resolution . they propose introducing a coarse-to-fine approach that incorporates a less accurate but more efficient bilinear factor . |
| Outcome: | The proposed model significantly improves accuracy on the English OntoNotes benchmark while being far more computationally efficient. |