Papers with SF
Fine-Tuning Medium-Scale LLMs for Joint Intent Classification and Slot Filling: A Data-Efficient and Cost-Effective Solution for SMEs (2025.coling-industry)
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| Challenge: | Current techniques for user comprehension in DS depend heavily on labeled data and the data annotation process for NLU is labor-intensive and requires expert annotators. |
| Approach: | They propose to fine-tune a model for joint Intent Classification and Slot Filling with only 10% of the data. |
| Outcome: | The proposed model outperforms existing models in monolingual and cross-lingual scenarios with only 10% of the data. |
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey (2020.coling-main)
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| Challenge: | In recent years, neural-network based models have been used for a wide range of tasks, including slot filling and intent classification. |
| Approach: | They propose three neural architectures to model slot filling and intent classification . they propose independent models, joint models and transfer learning models that exploit the mutual benefit of the two tasks simultaneously and scale the model to new domains. |
| Outcome: | The proposed models model SF and IC separately, exploit mutual benefit of the two tasks simultaneously and scale the model to new domains. |
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling (2021.findings-emnlp)
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| Challenge: | Intent classification and slot filling are key building blocks in task-oriented dialogue systems. |
| Approach: | They propose an explicit-joint and supervised-contrastive learning framework for few-shot intent classification and slot filling. |
| Outcome: | The proposed model extracts intent and slot representations via bidirectional interactions and extends prototypical network to achieve explicit-joint learning. |
Simple Semantic Annotation and Situation Frames: Two Approaches to Basic Text Understanding in LORELEI (L18-1)
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| Challenge: | Existing annotations for low resource languages are under-resourced for human language technology, but lack of resources does not correlate with lack of need for such technologies. |
| Approach: | They propose two types of semantic annotation for the DARPA Low Resource Languages for Emerging Incidents program: Simple Semantic Annotation (SSA) and Situation Frames (SF). |
| Outcome: | The proposed approaches are aimed at labeling basic semantic information relevant to humanitarian aid and disaster relief scenarios. |
A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling (P19-1)
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| Challenge: | Existing models for slot filling and intent detection lack bi-directional interrelated connections between the intent and slots. |
| Approach: | They propose a bi-directional interrelated model for slot filling and intent detection that uses an SF-ID network to establish direct connections between the two tasks to promote each other mutually. |
| Outcome: | The proposed model improves on ATIS and Snips datasets in sentence-level semantic frame accuracy and improves performance on the two tasks. |
Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling (2022.coling-1)
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| Challenge: | Existing approaches to multiple intent detection and slot filling focus on task-specific components to capture the relationships between intents and slots. |
| Approach: | They propose a Unified Generative framework that captures the relationships between intents and slots in an utterance and formulates the task as a question-answering problem. |
| Outcome: | The proposed framework surpasses baselines on full-data and multi-intent benchmarks on 5-shot and 10-shot scenarios. |
Introducing Semantics into Speech Encoders (2023.acl-long)
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Derek Xu, Shuyan Dong, Changhan Wang, Suyoun Kim, Zhaojiang Lin, Bing Liu, Akshat Shrivastava, Shang-Wen Li, Liang-Hsuan Tseng, Guan-Ting Lin, Alexei Baevski, Hung-yi Lee, Yizhou Sun, Wei Wang
| Challenge: | Existing self-supervised speech encoders contain primarily acoustic rather than semantic information. |
| Approach: | They propose a task-agnostic unsupervised way to incorporate semantic information from large language model (LLM) systems into self-supervised speech encoders without labeled audio transcriptions. |
| Outcome: | The proposed approach improves spoken language understanding (SLU) performance by over 5% on intent classification (IC), with modest gains in named entity resolution (NER) and slot filling (SF), and spoken question answering (SQA) score by over 22%. |
TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks (2022.lrec-1)
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| Challenge: | Approximately 1 in 6 Americans (or 48 million people) are sickened by foodborne illness each year. |
| Approach: | They propose to use Twitter's TWEET-FID dataset to create annotated datasets for multiple foodborne illness incident detection tasks. |
| Outcome: | The proposed dataset is the first publicly available annotated dataset for multiple foodborne illness incident detection tasks. |
ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler (2024.lrec-main)
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| Challenge: | State-of-the-art intent classification and slot filling methods rely on data-intensive deep learning models . large language models exhibit remarkable zero-shot performance across various natural language tasks. |
| Approach: | They propose an approach framing IC and SF as language generation tasks for instruction-LLMs with a more efficient SF-prompting method. |
| Outcome: | The proposed approach outperforms state-of-the-art IC+SF method and in-context learning methods with GPT3.5 (175B). |
INT: Establishing Information Transfer for Multilingual Intent Detection and Slot Filling (2025.findings-acl)
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Di Wu, Liting Jiang, Bohui Mao, Hongyan Xie, Haoxiang Su, Zhongjiang He, Ruiyu Fang, Shuangyong Song, Hao Huang, Xuelong Li
| Challenge: | Existing studies struggle to achieve performance comparable to that on high-resource languages due to inherent linguistic diversity of multilingual SLU tasks. |
| Approach: | They propose a multilingual information transfer network to solve these challenges . they propose to reformulate SF as a span prediction problem and introduce a slot-matching attention mechanism to achieve slot alignment across languages. |
| Outcome: | The proposed model outperforms baseline models on the MASSIVE and MASSIV-UG datasets in overall accuracy across all languages. |
Radical Allomorphy: Phonological Surface Forms without Phonology (2025.findings-emnlp)
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| Challenge: | Recent work typically frames morphophonology as generating surface forms from abstract underlying representations (URs) this theory-laden assumption is expensive to annotate, especially in low-resource settings. |
| Approach: | a new approach frames morphophonology as generating surface forms from abstract underlying representations by applying phonological rules or constraints. |
| Outcome: | The proposed model removes the need to posit or label URs and lets the model exploit the surface evidence directly. |
Difference in Task Performance on Sparse Speech Representations (2026.acl-long)
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| Challenge: | Existing methods for learning speech representations that are useful for a variety of downstream tasks have been extensively investigated in different domains. |
| Approach: | They propose to train Autoencoders with varying sparsity levels using three SSL features and evaluate them on six tasks of SUPERB: speech enhancement, speaker identification, speech Emotion Recognition, phone recognition, automatic speech recognition and slot filling. |
| Outcome: | The proposed model can be used to learn speech representations that are useful for a variety of downstream tasks. |