Improving Slot Filling in Spoken Language Understanding with Joint Pointer and Attention (P18-2)
Copied to clipboard
| Challenge: | Experimental results show the effectiveness of our slot filling model at addressing the OOV problem. |
| Approach: | They propose a generative neural network model for slot filling based on a sequence-to-sequence model and a pointer network. |
| Outcome: | The proposed model is able to predict slot values on spoken language data. |
Similar Papers
Slot-Gated Modeling for Joint Slot Filling and Intent Prediction (N18-2)
Copied to clipboard
| Challenge: | Existing approaches for slot filling and intent detection have independent attention weights, but they suffer from error propagation due to their independent models. |
| Approach: | They propose a slot gate that focuses on learning the relationship between intent and slot attention vectors to obtain better semantic frame results by the global optimization. |
| Outcome: | The proposed model significantly improves sentence-level semantic frame accuracy with 4.2% and 1.9% relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively. |
Spoken Language Understanding for Task-oriented Dialogue Systems with Augmented Memory Networks (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent research shows promising results by jointly learning of slot filling and intent detection tasks. |
| Approach: | They propose a way to combine slot filling and slot filler learning to achieve state-of-the-art results. |
| Outcome: | The proposed model outperforms existing methods on benchmark datasets and ATIS datasets. |
Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods analyze and compute features collectively for all slot types, and have no way to explain slot filling model decisions. |
| Approach: | They propose a method that learns to generate additional slot type specific features to improve accuracy and provides explanations for slot filling decisions for the first time in a joint NLU model. |
| Outcome: | The proposed model improves on two widely used datasets and provides an explanation for slot filling decisions for the first time. |
Joint Slot Filling and Intent Detection via Capsule Neural Networks (P19-1)
Copied to clipboard
| Challenge: | Existing models that label slots and detect intent do not preserve hierarchical relationship between words, slots, and intents. |
| Approach: | They propose a capsule-based neural network model which performs slot filling and intent detection via a dynamic routing-by-agreement schema. |
| Outcome: | The proposed model performs better than existing models and existing models on real-world datasets. |
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey (2020.coling-main)
Copied to clipboard
| 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. |
Synergistic Augmentation: Enhancing Cross-Domain Zero-Shot Slot Filling with Small Model-Assisted Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to slot filling are limited due to data scarcity and timeconsuming efforts. |
| Approach: | They propose a framework that harnesses the power of a small model to augment inferential capabilities of LLMs without additional training. |
| Outcome: | The proposed framework improves slot filling performance on a spoken language dataset and a NER dataset. |
SpeechLLMs for Large-scale Contextualized Zero-shot Slot Filling (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Slot filling is a key subtask in spoken language understanding (SLU) . recent advent of speech-based large language models has opened new avenues for speech understanding . |
| Approach: | They propose to improve slot-filling task by creating an empirical upper bound for the task . they propose to use a speech-based large language model to integrate speech and text modalities . |
| Outcome: | The proposed model improves slot filling performance while reducing generalization gaps. |
Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding (N19-3)
Copied to clipboard
| Challenge: | Neural networks are used to understand spoken language understanding (SLU) but it is difficult to recognize the slots of unknown words or ‘open-vocabulary’ slots because of the high cost of creating a manually tagged SLU dataset. |
| Approach: | They propose to use a recurrent neural network to nois slots for data augmentation by using an attention-based bi-directional recurrence neural network. |
| Outcome: | The proposed method achieves performance improvements of up to 0.57% and 3.25 in intent prediction (accuracy) and slot filling (f1-score) and 0.53% accuracy. |
Syntactic Graph Convolutional Network for Spoken Language Understanding (2020.coling-main)
Copied to clipboard
| Challenge: | Existing work on slot filling and intent detection builds joint models without prior knowledge of linguistic knowledge. |
| Approach: | They propose a joint model that integrates syntactic structure for learning slot filling and intent detection jointly. |
| Outcome: | The proposed model outperforms existing models on two public benchmark datasets and further improves on slot filling and intent detection. |
A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling (N18-2)
Copied to clipboard
| Challenge: | Intent detection and slot filling are two main tasks for building a spoken language understanding system. |
| Approach: | They propose to use a sequence to sequence model to generate both intent and slot filling tasks together to perform the two tasks jointly. |
| Outcome: | The proposed model achieves 0.5% intent accuracy improvement and 0.9 % slot filling improvement on the ATIS benchmark data. |