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Found 10 papers, 10 papers with code
Lexical Entrainment for Conversational Systems
To address this, we propose a new dataset, named MULTIWOZ-ENTR, and a measure for LE for conversational systems.
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning
Prompt tuning (PT), where a small amount of trainable soft (continuous) prompt vectors is affixed to the input of language models (LM), has shown promising results across various tasks and models for parameter-efficient fine-tuning (PEFT).
Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis
In recent years, language models (LMs) have made remarkable progress in advancing the field of natural language processing (NLP).
Self Contrastive Learning for Session-based Recommendation
Session-based recommendation, which aims to predict the next item of users' interest as per an existing sequence interaction of items, has attracted growing applications of Contrastive Learning (CL) with improved user and item representations.
Rethinking Semi-supervised Learning with Language Models
Semi-supervised learning (SSL) is a popular setting aiming to effectively utilize unlabelled data to improve model performance in downstream natural language processing (NLP) tasks.
When and What to Ask Through World States and Text Instructions: IGLU NLP Challenge Solution
We move towards this target with two sub-tasks, a classification task and a ranking task.
Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner
Language models (LMs) trained on vast quantities of unlabelled data have greatly advanced the field of natural language processing (NLP).
Attention-based Ingredient Phrase Parser
As virtual personal assistants have now penetrated the consumer market, with products such as Siri and Alexa, the research community has produced several works on task-oriented dialogue tasks such as hotel booking, restaurant booking, and movie recommendation.
Learning to Execute Actions or Ask Clarification Questions
In this paper, we extend the Minecraft Corpus Dataset by annotating all builder utterances into eight types, including clarification questions, and propose a new builder agent model capable of determining when to ask or execute instructions.
StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts
Our experiments demonstrate that state-of-the-art models on the bAbI dataset struggle on the StepGame dataset.
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Question Answering
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