Papers with MSP
Active Learning for Multilingual Semantic Parser (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing multilingual semantic parsing datasets are limited in translation effort due to data imbalance. |
| Approach: | They propose a first active learning procedure for multilingual semantic parsing (AL-MSP) it selects only a subset from existing datasets to be translated, they propose . |
| Outcome: | The proposed method significantly reduces translation costs with ideal selection methods. |
Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for OOD detection are overconfident for OD samples . lack of labeled OOD examples leads to poor prior knowledge about these unknown intents, making it challenging to detect OOD samples. |
| Approach: | They propose a Bayesian OOD detection framework to calibrate distribution uncertainty using Monte-Carlo Dropout. |
| Outcome: | The proposed framework gains 33.33% OOD F1 improvements with increasing only 0.41% inference time compared to previous methods. |
Multi-stage Pre-training over Simplified Multimodal Pre-training Models (2021.acl-long)
Copied to clipboard
| Challenge: | Existing multimodal pre-training models require large amounts of training data and have huge model sizes, making them impossible to apply in low-resource situations. |
| Approach: | They propose a multi-stage pre-training method which uses information at different granularities from word, phrase to sentence in both texts and images to pre-train a model in stages. |
| Outcome: | The proposed method outperforms the original model in Image-Text Retrieval task and outperformed the original LXMERT model in downstream tasks. |
HierPrompt: Zero-Shot Hierarchical Text Classification with LLM-Enhanced Prototypes (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for Hierarchical Text Classification are based on prototypes, but do not perform well due to ambiguity and impreciseness of category names. |
| Approach: | They propose a method that leverages hierarchy-aware prompts to instruct LLM to produce more representative and informative prototypes. |
| Outcome: | The proposed method outperforms existing methods on three benchmark datasets. |
Evaluation of African American Language Bias in Natural Language Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have shown that large language generation models disadvantaging African American Language (AAL) can be biased for certain language varieties, but there is little research on the impact of these biases on other languages. |
| Approach: | They evaluate how well LLMs understand African American Language (AAL) in comparison to white Mainstream English (WME) using a dataset of AAL texts from a variety of regions and contexts, they find dialectal bias in six pre-trained LLM. |
| Outcome: | The proposed models understand African American language in comparison to white mainstream English (WME) the proposed models have performance gaps on two tasks that are not matched by the model. |
MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators (2022.acl-long)
Copied to clipboard
| Challenge: | Prompting has been shown to be a promising approach for applying pre-trained language models to perform downstream tasks. |
| Approach: | They propose a method that divides the translation process into three stages using pre-trained language models. |
| Outcome: | The proposed method significantly improves translation performance of pre-trained language models on three translation tasks. |
Multi-Source Probing for Open-Domain Conversational Understanding (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing models of open-domain dialogue comprehension have limited conversational understanding and response generation. |
| Approach: | They propose a multi-source probing method to probe dialogue comprehension abilities of open-domain dialogue models. |
| Outcome: | The proposed method aggregates features from multiple sources to accomplish diverse task goals and conducts downstream tasks in a generative manner consistent with dialogue model pre-training to leverage model capabilities. |
Not All Demonstration Examples are Equally Beneficial: Reweighting Demonstration Examples for In-Context Learning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have recently gained the In-Context Learning ability . however, the quality of demonstration examples is usually uneven . |
| Approach: | They propose to determine optimal weights for demonstration examples and apply them during ICL. |
| Outcome: | The proposed approach outperforms conventional ICL on 8 classification tasks. |
Mixture of Soft Prompts for Controllable Data Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) generate fluent text when the target output follows natural language patterns. |
| Approach: | They propose a method that uses large language models to generate fluent text from a limited ontology rather than direct prediction by using soft prompts. |
| Outcome: | The proposed method produces diverse and natural text while preserving label semantics. |
Meta-Semantics Augmented Few-Shot Relational Learning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for few-shot relational learning on knowledge graphs focus on leveraging specific relational information, but rich semantics inherent in KGs have been overlooked. |
| Approach: | They propose a meta-learning framework that integrates meta-semantics with relational information for few-shot relational learning. |
| Outcome: | Extensive experiments on two real-world KG benchmarks validate the effectiveness of PromptMeta in adapting to new relations with limited supervision. |
Typology-Aware Multilingual Morphosyntactic Parsing with Joint Abstract Node Modeling (2026.acl-long)
Copied to clipboard
| Challenge: | UniDive 2025 Morphosyntactic Parsing (MSP) shared task unifies dependency structure, morphological features, and unrealized arguments. |
| Approach: | They propose a multilingual, typology-aware joint system that integrates word-type prediction, content-only parsing, morphological tagging, and an abstract-node component within a single architecture. |
| Outcome: | The proposed model outperforms the leading submission by 3.23 percentage points in MSLAS, 3.35 in LAS, and 1.78 in FEATS macro F1. |