Search Results for author:
Found 31 papers, 7 papers with code
SoDA: On-device Conversational Slot Extraction
We propose a novel on-device neural sequence labeling model which uses embedding-free projections and character information to construct compact word representations to learn a sequence model using a combination of bidirectional LSTM with self-attention and CRF.
Approximating 1-Wasserstein Distance with Trees
In this paper, we aim to approximate the 1-Wasserstein distance by the tree-Wasserstein distance (TWD), where TWD is a 1-Wasserstein distance with tree-based embedding and can be computed in linear time with respect to the number of nodes on a tree.
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection
Hate speech detection is complex; it relies on commonsense reasoning, knowledge of stereotypes, and an understanding of social nuance that differs from one culture to the next.
Cultural Vocal Bursts Intensity Prediction
Few-Shot Learning
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Improving In-Context Few-Shot Learning via Self-Supervised Training
Self-supervised pretraining has made few-shot learning possible for many NLP tasks.
Few-shot Learning with Multilingual Language Models
Large-scale generative language models such as GPT-3 are competitive few-shot learners.
Efficient Large Scale Language Modeling with Mixtures of Experts
This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings: in- and out-of-domain language modeling, zero- and few-shot priming, and full-shot fine-tuning.
Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs
In this paper, we discuss approaches to detecting when models have beliefs about the world, and we improve on methods for updating model beliefs to be more truthful, with a focus on methods based on learned optimizers or hypernetworks.
Fixed Support Tree-Sliced Wasserstein Barycenter
By contrast, the Wasserstein distance on a tree, called the tree-Wasserstein distance, can be computed in linear time and allows for the fast comparison of a large number of distributions.
ProFormer: Towards On-Device LSH Projection Based Transformers
At the heart of text based neural models lay word representations, which are powerful but occupy a lot of memory making it challenging to deploy to devices with memory constraints such as mobile phones, watches and IoT.
Environment-agnostic Multitask Learning for Natural Language Grounded Navigation
Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e. g., following natural language instructions or dialog.
SemEval-2013 Task 2: Sentiment Analysis in Twitter
To address this issue, we have proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two subtasks: A, an expression-level subtask, and B, a message-level subtask.
SemEval-2013 Task 4: Free Paraphrases of Noun Compounds
In this paper, we describe SemEval-2013 Task 4: the definition, the data, the evaluation and the results.
SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals
In this paper, we define the task, describe the creation of the datasets, and discuss the results of the participating 28 systems submitted by 10 teams.
ProSeqo: Projection Sequence Networks for On-Device Text Classification
Our model ProSeqo uses dynamic recurrent projections without the need to store or look up any pre-trained embeddings.
PRADO: Projection Attention Networks for Document Classification On-Device
We evaluate our approach on multiple large document text classification tasks.
Generalized Natural Language Grounded Navigation via Environment-agnostic Multitask Learning
Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e. g., following natural language instructions or dialog.
On-Device Text Representations Robust To Misspellings via Projections
Recently, there has been a strong interest in developing natural language applications that live on personal devices such as mobile phones, watches and IoT with the objective to preserve user privacy and have low memory.
On-device Structured and Context Partitioned Projection Networks
We show that this results in accelerated inference and performance improvements.
Transferable Neural Projection Representations
Neural word representations are at the core of many state-of-the-art natural language processing models.
Self-Governing Neural Networks for On-Device Short Text Classification
Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications.
Learning Steady-States of Iterative Algorithms over Graphs
Many graph analytics problems can be solved via iterative algorithms where the solutions are often characterized by a set of steady-state conditions.
Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension
Reading comprehension is a challenging task in natural language processing and requires a set of skills to be solved.
Variational Reasoning for Question Answering with Knowledge Graph
Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts.
