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Found 20 papers, 2 papers with code
Joint Goal Segmentation and Goal Success Prediction on Multi-Domain Conversations
To evaluate the performance of a multi-domain goal-oriented Dialogue System (DS), it is important to understand what the users’ goals are for the conversations and whether those goals are successfully achieved.
Personalized Search-based Query Rewrite System for Conversational AI
Query rewrite (QR) is an emerging component in conversational AI systems, reducing user defect.
Contextual Rephrase Detection for Reducing Friction in Dialogue Systems
Rephrase detection is used to identify the rephrases and has long been treated as a task with pairwise input, which does not fully utilize the contextual information (e. g. users’ implicit feedback).
Overcoming Catastrophic Forgetting During Domain Adaptation of Seq2seq Language Generation
Seq2seq language generation models that are trained offline with multiple domains in a sequential fashion often suffer from catastrophic forgetting.
MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context Learning
Large Language models (LLMs) have demonstrated impressive in-context learning (ICL) capabilities, where a LLM makes predictions for a given test input together with a few input-output pairs (demonstrations).
PersonaPKT: Building Personalized Dialogue Agents via Parameter-efficient Knowledge Transfer
Personalized dialogue agents (DAs) powered by large pre-trained language models (PLMs) often rely on explicit persona descriptions to maintain personality consistency.
CLICKER: Attention-Based Cross-Lingual Commonsense Knowledge Transfer
Recent advances in cross-lingual commonsense reasoning (CSR) are facilitated by the development of multilingual pre-trained models (mPTMs).
KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting
Query Rewriting (QR) plays a critical role in large-scale dialogue systems for reducing frictions.
PENTATRON: PErsonalized coNText-Aware Transformer for Retrieval-based cOnversational uNderstanding
Conversational understanding is an integral part of modern intelligent devices.
Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI
Self-learning paradigms in large-scale conversational AI agents tend to leverage user feedback in bridging between what they say and what they mean.
A Vocabulary-Free Multilingual Neural Tokenizer for End-to-End Task Learning
Additionally, the dependency on a fixed vocabulary limits the subword models' adaptability across languages and domains.
Query Expansion and Entity Weighting for Query Reformulation Retrieval in Voice Assistant Systems
However, these methods rarely focus on query expansion and entity weighting simultaneously, which may limit the scope and accuracy of the query reformulation retrieval.
Incremental user embedding modeling for personalized text classification
Individual user profiles and interaction histories play a significant role in providing customized experiences in real-world applications such as chatbots, social media, retail, and education.
VAE based Text Style Transfer with Pivot Words Enhancement Learning
Text Style Transfer (TST) aims to alter the underlying style of the source text to another specific style while keeping the same content.
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation
In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with weak supervision data.
Pattern-aware Data Augmentation for Query Rewriting in Voice Assistant Systems
Query rewriting (QR) systems are widely used to reduce the friction caused by errors in a spoken language understanding pipeline.
Personalized Query Rewriting in Conversational AI Agents
Spoken language understanding (SLU) systems in conversational AI agents often experience errors in the form of misrecognitions by automatic speech recognition (ASR) or semantic gaps in natural language understanding (NLU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Pre-Training for Query Rewriting in A Spoken Language Understanding System
Then, inspired by the wide success of pre-trained contextual language embeddings, and also as a way to compensate for insufficient QR training data, we propose a language-modeling (LM) based approach to pre-train query embeddings on historical user conversation data with a voice assistant.
Feedback-Based Self-Learning in Large-Scale Conversational AI Agents
Typically, the accuracy of the ML models in these components are improved by manually transcribing and annotating data.
Knowledge Distillation from Internal Representations
In this paper, we propose to distill the internal representations of a large model such as BERT into a simplified version of it.
