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Found 22 papers, 5 papers with code
ReALM: Reference Resolution As Language Modeling
Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds.
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking
Remarkably, our few-shot learning approach recovers nearly $98%$ of the performance compared to the few-shot setup using human-annotated training data.
Can Large Language Models Understand Context?
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent.
MARRS: Multimodal Reference Resolution System
Successfully handling context is essential for any dialog understanding task.
STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants
In the context of a voice assistant system, steering refers to the phenomenon in which a user issues a follow-up command attempting to direct or clarify a previous turn.
Intelligent Assistant Language Understanding On Device
It has recently become feasible to run personal digital assistants on phones and other personal devices.
5IDER: Unified Query Rewriting for Steering, Intent Carryover, Disfluencies, Entity Carryover and Repair
Providing voice assistants the ability to navigate multi-turn conversations is a challenging problem.
On Efficiently Acquiring Annotations for Multilingual Models
When tasked with supporting multiple languages for a given problem, two approaches have arisen: training a model for each language with the annotation budget divided equally among them, and training on a high-resource language followed by zero-shot transfer to the remaining languages.
MMIU: Dataset for Visual Intent Understanding in Multimodal Assistants
In multimodal assistant, where vision is also one of the input modalities, the identification of user intent becomes a challenging task as visual input can influence the outcome.
Using Pause Information for More Accurate Entity Recognition
Entity tags in human-machine dialog are integral to natural language understanding (NLU) tasks in conversational assistants.
DEXTER: Deep Encoding of External Knowledge for Named Entity Recognition in Virtual Assistants
Named entity recognition (NER) is usually developed and tested on text from well-written sources.
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues
In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding.
Noise Robust Named Entity Understanding for Voice Assistants
Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries.
Learning to Relate from Captions and Bounding Boxes
In this work, we propose a novel approach that predicts the relationships between various entities in an image in a weakly supervised manner by relying on image captions and object bounding box annotations as the sole source of supervision.
LucidDream: Controlled Temporally-Consistent DeepDream on Videos
In this work, we aim to propose a set of techniques to improve the controllability and aesthetic appeal when DeepDream, which uses a pre-trained neural network to modify images by hallucinating objects into them, is applied to videos.
Weakly Supervised Attention Networks for Entity Recognition
The task of entity recognition has traditionally been modelled as a sequence labelling task.
Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces
We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) --- a semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique.
Compression and Localization in Reinforcement Learning for ATARI Games
Deep neural networks have become commonplace in the domain of reinforcement learning, but are often expensive in terms of the number of parameters needed.
BLISS in Non-Isometric Embedding Spaces
We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) --- a novel semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique.
Unsupervised Depth Estimation, 3D Face Rotation and Replacement
We present an unsupervised approach for learning to estimate three dimensional (3D) facial structure from a single image while also predicting 3D viewpoint transformations that match a desired pose and facial geometry.
Nested LSTMs
We propose Nested LSTMs (NLSTM), a novel RNN architecture with multiple levels of memory.
MMGAN: Manifold Matching Generative Adversarial Network
MMGAN finds two manifolds representing the vector representations of real and fake images.
