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Found 9 papers, 0 papers with code
Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering
However, even skilled domain experts are often unable to foresee all possible user intents at design time and for practical applications, novel intents may have to be inferred incrementally on-the-fly from user utterances.
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Domain Adaptation for NMT via Filtered Iterative Back-Translation
In comparison, in this work, we observe that a simpler filtering approach based on a domain classifier, applied only to the pseudo-training data can consistently perform better, providing performance gains of 1. 40, 1. 82 and 0. 76 in terms of BLEU score for Medical, Law and IT in one direction, and 1. 28, 1. 60 and 1. 60 in the other direction in low resource scenario over competitive baselines.
Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection
This typically entails repeated retraining of the intent detector on both the existing and novel intents which can be expensive and would require storage of all past data corresponding to prior intents.
Improving NMT via Filtered Back Translation
This is further followed by fine-tuning on the domain-specific corpus.
Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning
Additional experiments in the in-domain setting show that FuSIC-KBQA also outperforms SoTA KBQA models when training data is limited.
Do I have the Knowledge to Answer? Investigating Answerability of Knowledge Base Questions
When answering natural language questions over knowledge bases, missing facts, incomplete schema and limited scope naturally lead to many questions being unanswerable.
Complex Question Answering on knowledge graphs using machine translation and multi-task learning
Question answering (QA) over a knowledge graph (KG) is a task of answering a natural language (NL) query using the information stored in KG.
From Monolingual to Multilingual FAQ Assistant using Multilingual Co-training
We observe that the proposed approach provides consistent gains in the performance of BERT for multiple benchmark datasets (e. g. 1. 0{\%} gain on MLDocs, and 1. 2{\%} gain on XNLI over translate-train with BERT), while requiring a single model for multiple languages.
