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Found 27 papers, 9 papers with code
AstBERT: Enabling Language Model for Financial Code Understanding with Abstract Syntax Trees
We evaluate the performance of the proposed model on three tasks, including code question answering, code clone detection and code refinement.
SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples
To alleviate feature suppression, we propose contrastive learning for unsupervised sentence embedding with soft negative samples (SNCSE).
Exploring Retraining-Free Speech Recognition for Intra-sentential Code-Switching
To achieve such an ambitious goal, new mechanisms for foreign pronunciation generation and language model (LM) enrichment have been devised.
Revisiting Knowledge Distillation: An Inheritance and Exploration Framework
The inheritance part is learned with a similarity loss to transfer the existing learned knowledge from the teacher model to the student model, while the exploration part is encouraged to learn representations different from the inherited ones with a dis-similarity loss.
Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization
Specifically, we introduce Gait recognition as an auxiliary task to drive the Image ReID model to learn cloth-agnostic representations by leveraging personal unique and cloth-independent gait information, we name this framework as GI-ReID.
3D Local Convolutional Neural Networks for Gait Recognition
Second, different body parts possess different scales, and even the same part in different frames can appear at different locations and scales.
Argumentation Mining on Essays at Multi Scales
Recent research mainly models the task as a sequence tagging problem and deal with all the argumentation components at word level.
Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition
We tackle implicit discourse relation recognition.
Using a Penalty-based Loss Re-estimation Method to Improve Implicit Discourse Relation Classification
We tackle implicit discourse relation classification, a task of automatically determining semantic relationships between arguments.
Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition
The topology of the adjacency graph is a key factor for modeling the correlations of the input skeletons.
Next Waves in Veridical Network Embedding
Embedding nodes of a large network into a metric (e. g., Euclidean) space has become an area of active research in statistical machine learning, which has found applications in natural and social sciences.
Topological Hall Effect and Skyrmion-like Bubbles at a Charge-transfer Interface
Here, the emergence, tuning and interpretation of hump-shape Hall Effect from a CaMnO3/CaIrO3/CaMnO3 trilayer structure are studied in detail.
Mesoscale and Nanoscale Physics
Language Model-Driven Unsupervised Neural Machine Translation
We present the detailed mathematical construction of our method.
SubCharacter Chinese-English Neural Machine Translation with Wubi encoding
However, when the translation task involves Chinese, semantic granularity remains at the word and character level, so there is still need more fine-grained translation model of Chinese.
SNDCNN: Self-normalizing deep CNNs with scaled exponential linear units for speech recognition
The most popular way to train very deep CNNs is to use shortcut connections (SC) together with batch normalization (BN).
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning
Rapid progress has been made in the field of reading comprehension and question answering, where several systems have achieved human parity in some simplified settings.
Ranked #5 on
Question Answering
on DROP Test
Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension
This paper considers the reading comprehension task in which multiple documents are given as input.
Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification
Open-domain targeted sentiment analysis aims to detect opinion targets along with their sentiment polarities from a sentence.
Aspect-Based Sentiment Analysis
Aspect Term Extraction and Sentiment Classification
+1
Attention-Guided Answer Distillation for Machine Reading Comprehension
Despite that current reading comprehension systems have achieved significant advancements, their promising performances are often obtained at the cost of making an ensemble of numerous models.
Read + Verify: Machine Reading Comprehension with Unanswerable Questions
Machine reading comprehension with unanswerable questions aims to abstain from answering when no answer can be inferred.
Ranked #12 on
Question Answering
on SQuAD2.0 dev
Free-rider Episode Screening via Dual Partition Model
An effective technique for filtering free-rider episodes is using a partition model to divide an episode into two consecutive subepisodes and comparing the observed support of such episode with its expected support under the assumption that these two subepisodes occur independently.
BinPro: A Tool for Binary Source Code Provenance
Enforcing open source licenses such as the GNU General Public License (GPL), analyzing a binary for possible vulnerabilities, and code maintenance are all situations where it is useful to be able to determine the source code provenance of a binary.
Cryptography and Security D.4.6
Ocasta: Clustering Configuration Settings For Error Recovery
However, a recent study found that a significant amount of configuration errors require fixing more than one setting together.
Reinforced Mnemonic Reader for Machine Reading Comprehension
In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects.
Ranked #17 on
Question Answering
on TriviaQA
Multi-Objective Learning and Mask-Based Post-Processing for Deep Neural Network Based Speech Enhancement
We propose a multi-objective framework to learn both secondary targets not directly related to the intended task of speech enhancement (SE) and the primary target of the clean log-power spectra (LPS) features to be used directly for constructing the enhanced speech signals.
Sound
Maximum a Posteriori Adaptation of Network Parameters in Deep Models
We present a Bayesian approach to adapting parameters of a well-trained context-dependent, deep-neural-network, hidden Markov model (CD-DNN-HMM) to improve automatic speech recognition performance.
Feature Selection with Redundancy-complementariness Dispersion
Feature selection has attracted significant attention in data mining and machine learning in the past decades.
