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Found 29 papers, 12 papers with code
Enhancing Grammatical Error Correction Systems with Explanations
Grammatical error correction systems improve written communication by detecting and correcting language mistakes.
Contrastive Learning of Sentence Embeddings from Scratch
Contrastive learning has been the dominant approach to train state-of-the-art sentence embeddings.
Instance Smoothed Contrastive Learning for Unsupervised Sentence Embedding
Contrastive learning-based methods, such as unsup-SimCSE, have achieved state-of-the-art (SOTA) performances in learning unsupervised sentence embeddings.
SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support
There has been an increasing research interest in developing specialized dialogue systems that can offer mental health support.
Towards Automated Real-time Evaluation in Text-based Counseling
However, these methods often demand large scale and high quality counseling data, which are difficult to collect.
S-SimCSE: Sampled Sub-networks for Contrastive Learning of Sentence Embedding
The corresponding outputs, two sentence embeddings derived from the same sentence with different dropout masks, can be used to build a positive pair.
Dynamic Resolution Network
To this end, we propose a novel dynamic-resolution network (DRNet) in which the input resolution is determined dynamically based on each input sample.
Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems
The current state-of-the-art ranking methods mainly use an encoding paradigm called Cross-Encoder, which separately encodes each context-candidate pair and ranks the candidates according to their fitness scores.
Do Transformer Modifications Transfer Across Implementations and Applications?
The research community has proposed copious modifications to the Transformer architecture since it was introduced over three years ago, relatively few of which have seen widespread adoption.
Memformer: A Memory-Augmented Transformer for Sequence Modeling
Transformers have reached remarkable success in sequence modeling.
Attention that does not Explain Away
Models based on the Transformer architecture have achieved better accuracy than the ones based on competing architectures for a large set of tasks.
CLUE: A Chinese Language Understanding Evaluation Benchmark
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
Talking-Heads Attention
We introduce "talking-heads attention" - a variation on multi-head attention which includes linearprojections across the attention-heads dimension, immediately before and after the softmax operation. While inserting only a small number of additional parameters and a moderate amount of additionalcomputation, talking-heads attention leads to better perplexities on masked language modeling tasks, aswell as better quality when transfer-learning to language comprehension and question answering tasks.
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.
Ranked #1 on
Natural Language Inference
on QNLI
Multi-stage Pretraining for Abstractive Summarization
Neural models for abstractive summarization tend to achieve the best performance in the presence of highly specialized, summarization specific modeling add-ons such as pointer-generator, coverage-modeling, and inferencetime heuristics.
Video Representation Learning and Latent Concept Mining for Large-scale Multi-label Video Classification
We report on CMU Informedia Lab's system used in Google's YouTube 8 Million Video Understanding Challenge.
Hidden Two-Stream Convolutional Networks for Action Recognition
State-of-the-art action recognition approaches rely on traditional optical flow estimation methods to pre-compute motion information for CNNs.
Ranked #18 on
Action Recognition
on UCF101
Guided Optical Flow Learning
We study the unsupervised learning of CNNs for optical flow estimation using proxy ground truth data.
Deep Local Video Feature for Action Recognition
We investigate the problem of representing an entire video using CNN features for human action recognition.
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search.
Improving Human Activity Recognition Through Ranking and Re-ranking
We propose two well-motivated ranking-based methods to enhance the performance of current state-of-the-art human activity recognition systems.
Handcrafted Local Features are Convolutional Neural Networks
We approach this problem by first showing that local handcrafted features and Convolutional Neural Networks (CNNs) share the same convolution-pooling network structure.
Beyond Spatial Pyramid Matching: Space-time Extended Descriptor for Action Recognition
We address the problem of generating video features for action recognition.
The Best of Both Worlds: Combining Data-independent and Data-driven Approaches for Action Recognition
First, we propose a two-stream Stacked Convolutional Independent Subspace Analysis (ConvISA) architecture to show that unsupervised learning methods can significantly boost the performance of traditional local features extracted from data-independent models.
Training Binary Multilayer Neural Networks for Image Classification using Expectation Backpropagation
In this paper, we investigate the capability of BMNNs using the EBP algorithm on multiclass image classification tasks.
Long-short Term Motion Feature for Action Classification and Retrieval
Therefore, they need to occur frequently enough in the videos and to be be able to tell the difference among different types of motions.
Self-Paced Learning with Diversity
Self-paced learning (SPL) is a recently proposed learning regime inspired by the learning process of humans and animals that gradually incorporates easy to more complex samples into training.
Beyond Gaussian Pyramid: Multi-skip Feature Stacking for Action Recognition
MIFS compensates for information lost from using differential operators by recapturing information at coarse scales.
Temporal Extension of Scale Pyramid and Spatial Pyramid Matching for Action Recognition
To achieve temporal scale invariance, we develop a method called temporal scale pyramid (TSP).
