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Found 20 papers, 12 papers with code
Do Encoder Representations of Generative Dialogue Models have sufficient summary of the Information about the task ?
Predicting the next utterance in dialogue is contingent on encoding of users’ input text to generate appropriate and relevant response in data-driven approaches.
Sometimes We Want Ungrammatical Translations
Rapid progress in Neural Machine Translation (NMT) systems over the last few years has focused primarily on improving translation quality, and as a secondary focus, improving robustness to perturbations (e. g. spelling).
Towards Practical Tool Usage for Continually Learning LLMs
Large language models (LLMs) show an innate skill for solving language based tasks.
Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games
In this work, we explore and evaluate updating LLM used for candidate recommendation during the learning of the text based game as well to mitigate the reliance on the human annotated gameplays, which are costly to acquire.
Measuring the Knowledge Acquisition-Utilization Gap in Pretrained Language Models
We propose a systematic framework to measure parametric knowledge utilization in PLMs.
Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness
The fairness achieved by our method surpasses that of data augmentation on three text classification datasets, using no more than half of the examples in the augmented dataset.
Local Structure Matters Most in Most Languages
In this work, we replicate a study on the importance of local structure, and the relative unimportance of global structure, in a multilingual setting.
Detecting Languages Unintelligible to Multilingual Models through Local Structure Probes
However, this transfer is not universal, with many languages not currently understood by multilingual approaches.
Local Structure Matters Most: Perturbation Study in NLU
Recent research analyzing the sensitivity of natural language understanding models to word-order perturbations has shown that neural models are surprisingly insensitive to the order of words.
A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic loss
Neural models trained for next utterance generation in dialogue task learn to mimic the n-gram sequences in the training set with training objectives like negative log-likelihood (NLL) or cross-entropy.
Memory Augmented Optimizers for Deep Learning
Popular approaches for minimizing loss in data-driven learning often involve an abstraction or an explicit retention of the history of gradients for efficient parameter updates.
Do Encoder Representations of Generative Dialogue Models Encode Sufficient Information about the Task ?
Predicting the next utterance in dialogue is contingent on encoding of users' input text to generate appropriate and relevant response in data-driven approaches.
Sometimes We Want Translationese
Rapid progress in Neural Machine Translation (NMT) systems over the last few years has been driven primarily towards improving translation quality, and as a secondary focus, improved robustness to input perturbations (e. g. spelling and grammatical mistakes).
UnNatural Language Inference
We provide novel evidence that complicates this claim: we find that state-of-the-art Natural Language Inference (NLI) models assign the same labels to permuted examples as they do to the original, i. e. they are largely invariant to random word-order permutations.
On Task-Level Dialogue Composition of Generative Transformer Model
In this work, we begin by studying the effect of training human-human task-oriented dialogues towards improving the ability to compose multiple tasks on Transformer generative models.
How To Evaluate Your Dialogue System: Probe Tasks as an Alternative for Token-level Evaluation Metrics
To bridge this gap in evaluation, we propose designing a set of probing tasks to evaluate dialogue models.
Learning an Unreferenced Metric for Online Dialogue Evaluation
Evaluating the quality of a dialogue interaction between two agents is a difficult task, especially in open-domain chit-chat style dialogue.
The RLLChatbot: a solution to the ConvAI challenge
This article presents in detail the RLLChatbot that participated in the 2017 ConvAI challenge.
Extending Neural Generative Conversational Model using External Knowledge Sources
The use of connectionist approaches in conversational agents has been progressing rapidly due to the availability of large corpora.
MACA: A Modular Architecture for Conversational Agents
We propose a software architecture designed to ease the implementation of dialogue systems.
