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Found 30 papers, 11 papers with code
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model
In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various tasks.
Ranked #14 on
Natural Language Inference
on CommitmentBank
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9. 3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system.
Cross-Lingual Natural Language Inference
intent-classification
+5
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
We present the MASSIVE dataset--Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation.
Ranked #1 on
Slot Filling
on MASSIVE
TEACh: Task-driven Embodied Agents that Chat
Robots operating in human spaces must be able to engage in natural language interaction with people, both understanding and executing instructions, and using conversation to resolve ambiguity and recover from mistakes.
Generative Conversational Networks
We show an average improvement of 35% in intent detection and 21% in slot tagging over a baseline model trained from the seed data.
Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic Representation
Most existing work for Guesser encode the dialog history as a whole and train the Guesser models from scratch on the GuessWhat?!
Correcting Automated and Manual Speech Transcription Errors using Warped Language Models
Masked language models have revolutionized natural language processing systems in the past few years.
Are We There Yet? Learning to Localize in Embodied Instruction Following
Embodied instruction following is a challenging problem requiring an agent to infer a sequence of primitive actions to achieve a goal environment state from complex language and visual inputs.
Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational Agents
The second component of the research is the construction of a conversational agent model capable of injecting social language into an agent's responses while still preserving content.
Interactive Teaching for Conversational AI
Current conversational AI systems aim to understand a set of pre-designed requests and execute related actions, which limits them to evolve naturally and adapt based on human interactions.
LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question Answering
We show that LRTA makes a step towards truly understanding the question while the state-of-the-art model tends to learn superficial correlations from the training data.
Language Model is All You Need: Natural Language Understanding as Question Answering
Different flavors of transfer learning have shown tremendous impact in advancing research and applications of machine learning.
Warped Language Models for Noise Robust Language Understanding
The insertion and drop modification of the input text during training of WLM resemble the types of noise due to Automatic Speech Recognition (ASR) errors, and as a result WLMs are likely to be more robust to ASR noise.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Controllable Text Generation with Focused Variation
This work introduces Focused-Variation Network (FVN), a novel model to control language generation.
Improving Embedding Extraction for Speaker Verification with Ladder Network
Speaker verification is an established yet challenging task in speech processing and a very vibrant research area.
Multi-Task Siamese Neural Network for Improving Replay Attack Detection
It can be shown that SNN outperform the baseline by relative 26. 8 % Equal Error Rate (EER).
Joint Contextual Modeling for ASR Correction and Language Understanding
As a baseline approach, we trained task-specific Statistical Language Models (SLM) and fine-tuned state-of-the-art Generalized Pre-training (GPT) Language Model to re-rank the n-best ASR hypotheses, followed by a model to identify the dialog act and slots.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Exploration Based Language Learning for Text-Based Games
In this work, we propose to use the exploration approach of Go-Explore for solving text-based games.
Plato Dialogue System: A Flexible Conversational AI Research Platform
Plato has been designed to be easy to understand and debug and is agnostic to the underlying learning frameworks that train each component.
Flexibly-Structured Model for Task-Oriented Dialogues
It is based on a simple and practical yet very effective sequence-to-sequence approach, where language understanding and state tracking tasks are modeled jointly with a structured copy-augmented sequential decoder and a multi-label decoder for each slot.
OCC: A Smart Reply System for Efficient In-App Communications
Our system consists of two major components: intent detection and reply retrieval, which are very different from standard smart reply systems where the task is to directly predict a reply.
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning
and their own objectives, and can only interact via natural language they generate.
User Modeling for Task Oriented Dialogues
We further develop several variants by utilizing a latent variable model to inject random variations into user responses to promote diversity in simulated user responses and a novel goal regularization mechanism to penalize divergence of user responses from the initial user goal.
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems
To address this challenge, we propose a hybrid imitation and reinforcement learning method, with which a dialogue agent can effectively learn from its interaction with users by learning from human teaching and feedback.
End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning
We show that deep RL based optimization leads to significant improvement on task success rate and reduction in dialogue length comparing to supervised training model.
Towards Zero-Shot Frame Semantic Parsing for Domain Scaling
While multi-task training of such models alleviates the need for large in-domain annotated datasets, bootstrapping a semantic parsing model for a new domain using only the semantic frame, such as the back-end API or knowledge graph schema, is still one of the holy grail tasks of language understanding for dialogue systems.
Sequential Dialogue Context Modeling for Spoken Language Understanding
We compare the performance of our proposed architecture with two context models, one that uses just the previous turn context and another that encodes dialogue context in a memory network, but loses the order of utterances in the dialogue history.
Goal-Oriented Dialogue Systems
Spoken Language Understanding
Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks
Natural language understanding (NLU) is a core component of a spoken dialogue system.
Zero-Shot Learning for Semantic Utterance Classification
We propose a novel zero-shot learning method for semantic utterance classification (SUC).
