Papers with dialog state tracking
Explicit Retrofitting of Distributional Word Vectors (P18-1)
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| Challenge: | Existing models for word vector specialization focus on word co-occurrences from large text corpora, resulting in a tendency to fuse semantic similarity with other types of semantic relatedness. |
| Approach: | They propose to transform external lexico-semantic relations into training examples and learn an explicit retrofitting model. |
| Outcome: | The proposed model can specialize vector spaces of new languages and translate them to other languages. |
PizzaPal: Conversational Pizza Ordering using a High-Density Conversational AI Platform (D18-2)
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| Challenge: | a pizza ordering bot that can be used to order pizzas is described in this paper. |
| Approach: | They describe PizzaPal, a voice-only agent for ordering pizza, and the Conversational AI architecture built at b4.ai. |
| Outcome: | The pizza ordering bot is based on a dialog framework developed by b4.ai . |
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization (D18-1)
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| Challenge: | Semantic specialization is a process of fine-tuning pre-trained distributional word vectors using external lexical knowledge to accentuate a particular semantic relation in the specialized vector space. |
| Approach: | They propose a method for specializing distributional word vectors using external lexical knowledge. |
| Outcome: | The proposed method improves on word similarity, dialog state tracking, and lexical simplification across three languages and on three tasks. |
Enhancing Word Embeddings with Knowledge Extracted from Lexical Resources (2020.acl-srw)
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| Challenge: | In this paper, we present an effective method for semantic specialization of word vector representations. |
| Approach: | They propose a method for semantic specialization of word vector representations using BabelNet. |
| Outcome: | The proposed method improves on word similarity and dialog state tracking tasks. |
A Comparative Study on Schema-Guided Dialogue State Tracking (2021.naacl-main)
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| Challenge: | Recent work proposes using natural language descriptions to define domain ontologies for dialog state tracking. |
| Approach: | They propose to use natural language descriptions to define domain ontologies instead of tag names for each intent or slot . they introduce a set of newly designed bench-marking descriptions and show model robustness . |
| Outcome: | The proposed model is robust on homogeneous and heterogeneously described descriptions in training and evaluation. |
DS-TOD: Efficient Domain Specialization for Task-Oriented Dialog (2022.findings-acl)
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| Challenge: | Recent work shows that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over traditional language modeling (LM) pretraining. |
| Approach: | They propose a resource-efficient and modular domain specialization by means of domain adapters in which domain knowledge is encoded. |
| Outcome: | The proposed framework extracts domain-specific terms and then uses them to build DomainCC and DomainReddit resources based on masked language modeling and response selection objectives. |
Continual Prompt Tuning for Dialog State Tracking (2022.acl-long)
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| Challenge: | Existing methods to train a model on a sequence of tasks are not efficient enough to mitigate catastrophic forgetting. |
| Approach: | They propose a parameter-efficient framework that prevents forgetting and enables knowledge transfer between tasks by learning and freezing a pre-trained model. |
| Outcome: | The proposed framework avoids forgetting and enables knowledge transfer between tasks. |
ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity? (2023.acl-short)
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Michael Heck, Nurul Lubis, Benjamin Ruppik, Renato Vukovic, Shutong Feng, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Milica Gasic
| Challenge: | Recent research on dialog state tracking (DST) focuses on methods that allow few- and zero-shot transfer to new domains or schemas. |
| Approach: | They propose to use schema descriptions to facilitate zero-shot transfer to new domains . they argue that general purpose language models lack the ability to replace specialized systems . |
| Outcome: | The proposed method achieves state-of-the-art in zero-shot DST with in-context learning capabilities. |
Unsupervised Slot Schema Induction for Task-oriented Dialog (2022.naacl-main)
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| Challenge: | Defining task-specific schemas is the first step of building a task-oriented dialog system. |
| Approach: | They propose an unsupervised approach for slot schema induction from unlabeled dialog corpora using in-domain language models and unsupervised parsing structures. |
| Outcome: | The proposed method shows significant performance improvement on multi-domain and SGD datasets. |
Situated and Interactive Multimodal Conversations (2020.coling-main)
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Seungwhan Moon, Satwik Kottur, Paul Crook, Ankita De, Shivani Poddar, Theodore Levin, David Whitney, Daniel Difranco, Ahmad Beirami, Eunjoon Cho, Rajen Subba, Alborz Geramifard
| Challenge: | Situated Interactive MultiModal Conversations (SIMMC) is a new direction for virtual assistants that handle multimodal inputs and perform multimodal actions. |
| Approach: | They propose to use Situated Interactive MultiModal Conversations (SIMMC) to train agents to take multimodal actions grounded in a co-evolving multimodal context. |
| Outcome: | The proposed model will be made publicly available. |
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking (2021.acl-short)
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| Challenge: | Existing dialog state tracking models neglect rich structural information in a dataset. |
| Approach: | They propose to use curriculum learning to leverage dialog state tracking data . they propose a model-agnostic framework that pre-trains a DST model with schema information . |
| Outcome: | The proposed framework improves performance over a transformer-based and RNN-based model on WOZ2.0 and MultiWOZ2.1. |
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems (2021.emnlp-main)
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| Challenge: | Large-scale pre-trained language models have shown promising results for few-shot learning in task-oriented dialog (ToD) systems. |
| Approach: | They propose a self-training approach that iteratively labels the most confident unlabeled data to train a stronger Student model. |
| Outcome: | The proposed approach improves state-of-the-art pre-trained models in few-shot learning scenarios for task-oriented dialog (ToD) systems when only a small number of labeled data are available. |
Cross-lingual Semantic Specialization via Lexical Relation Induction (D19-1)
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| Challenge: | Semantic specialization is not available in many languages because of their incomplete or non-existent structure. |
| Approach: | They propose a method that transfers specialization from a resource-rich source language to virtually any target language. |
| Outcome: | The proposed method performs lexical simplification, dialog state tracking, and textual similarity tasks in 5 languages. |
Knowledge-grounded Dialog State Tracking (2022.findings-emnlp)
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| Challenge: | Structured knowledge is encoded implicitly into model parameters for downstream tasks, making training inefficient. |
| Approach: | They propose to perform dialog state tracking grounded on knowledge encoded externally. |
| Outcome: | The proposed method outperforms baseline models in the few-shot learning setting. |
Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation (2020.emnlp-main)
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| Challenge: | Recent studies have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks. |
| Approach: | They propose a Variational Hierarchical Dialog Autoencoder for modeling the complete aspects of goal-oriented dialogs using inter-connected latent variables and learns to generate coherent dialogs from the latent spaces. |
| Outcome: | The proposed model outperforms previous strong baselines on dialog response generation and user simulation tasks. |
Conversational Semantic Parsing for Dialog State Tracking (2020.emnlp-main)
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Jianpeng Cheng, Devang Agrawal, Héctor Martínez Alonso, Shruti Bhargava, Joris Driesen, Federico Flego, Dain Kaplan, Dimitri Kartsaklis, Lin Li, Dhivya Piraviperumal, Jason D. Williams, Hong Yu, Diarmuid Ó Séaghdha, Anders Johannsen
| Challenge: | Language understanding for task-based dialog systems is often termed "dialog state tracking" (DST) whereas semantic parsing is the task of converting a single-turn utterance to a graphstructured meaning representation, DST is more complex. |
| Approach: | They propose a framework for dialog state tracking that incorporates semantic compositionality, cross-domain knowledge sharing and co-reference. |
| Outcome: | The proposed framework improves on state-of-the-art approaches for dialog state tracking (DST) it incorporates semantic compositionality, cross-domain knowledge sharing and co-reference. |