Papers by Jiangming Liu
Text Generation from Discourse Representation Structures (2021.naacl-main)
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| Challenge: | Existing models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs) . |
| Approach: | They propose neural models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs). |
| Outcome: | The proposed model achieves competitive performance on the GMB benchmark against several strong baselines. |
Multi-Step Inference for Reasoning Over Paragraphs (2020.emnlp-main)
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| Challenge: | Existing models for complex reasoning use symbols or black-box transformers . a compositional model can chain together free-form predicates and logical connectives . |
| Approach: | They propose a compositional model that finds relevant sentences and then chains them together using neural modules. |
| Outcome: | The proposed model improves performance on a recently-introduced dataset. |
Dscorer: A Fast Evaluation Metric for Discourse Representation Structure Parsing (2020.acl-main)
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| Challenge: | Discourse representation structures (DRSs) are scoped semantic representations for texts of arbitrary length. |
| Approach: | They propose a new metric which converts box-style DRSs to graphs and measures the overlap of n-grams. |
| Outcome: | Experiments show that Dscorer computes accuracy scores that are correlated with Counter at a fraction of the time. |
Learning Domain Representation for Multi-Domain Sentiment Classification (N18-1)
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| Challenge: | Training data for sentiment analysis is abundant in multiple domains, yet scarce for other domains. |
| Approach: | They propose to use domain-specific representations of input sentences to improve sentiment classification . they use a descriptor vector to map adversarially trained domain-general Bi-LSTM inputs into domain- specific representations . |
| Outcome: | The proposed model outperforms existing methods on multi-domain sentiment analysis significantly. |
DRTS Parsing with Structure-Aware Encoding and Decoding (2020.acl-main)
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| Challenge: | Discourse representation tree structure (DRTS) parsing is a new semantic parser which ignores structural information. |
| Approach: | They propose a structural-aware model to integrate structural information into the model . they use graph attention network (GAT) to exploit structural information for effective modeling . |
| Outcome: | The proposed model can achieve the best performance on a benchmark dataset. |
Soft Well-Formed Semantic Parsing with Score-Based Selection (2024.lrec-main)
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| Challenge: | Semantic parsing is the task of translating natural language into a structured, formal semantic representation that can be interpreted by machines. |
| Approach: | They propose a score-based method to select well-formed outputs from candidates generated by beam search algorithms. |
| Outcome: | The proposed method reduces the number of ill-formed outputs and improves F1 scores in English. |
Discourse Representation Parsing for Sentences and Documents (P19-1)
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| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
| Approach: | They propose a neural model which parses discourse structures of arbitrary length and granularity. |
| Outcome: | The proposed model outperforms baseline models on sentence- and document-level benchmarks. |
Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020.findings-emnlp)
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Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou
| Challenge: | Standard test sets for supervised learning evaluate in-distribution generalization but are misleading when a dataset has systematic gaps. |
| Approach: | They propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data. |
| Outcome: | The proposed model performs significantly lower on contrast sets than on the original test sets—up to 25% in some cases. |
Model-Agnostic Cross-Lingual Training for Discourse Representation Structure Parsing (2024.lrec-main)
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| Challenge: | Discourse Representation Structure (DRS) parsers are constrained when trained exclusively on monolingual data. |
| Approach: | They propose a cross-lingual training strategy that leverages cross-linguistic training data to train models in multiple languages. |
| Outcome: | The proposed method improves clause and graph parsing in English, German, Italian and Dutch. |
FedID: Federated Interactive Distillation for Large-Scale Pretraining Language Models (2023.emnlp-main)
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| Challenge: | federated learning (FL) is widely studied in user-related natural language processing (NLP) but its performance is faded by confirmation bias. |
| Approach: | They propose a decentralized learning paradigm that uses labeled data to rectify local models . they propose federated interactive distillation (FedID) to alleviate communication overhead . |
| Outcome: | The proposed framework achieves the best results in homogeneous and heterogeneously federated scenarios. |
Discourse Representation Structure Parsing (P18-1)
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| Challenge: | Existing semantic parsers are data-driven using annotated examples consisting of utterances and their meaning representations. |
| Approach: | They propose a method which transforms Discourse Representation Structures (DRSs) to trees and develop a structure-aware model which decomposes the decoding process into three stages. |
| Outcome: | The proposed model outperforms baseline models on the Groningen Meaning Bank (GMB) by a wide margin. |