Search Results for author:
Found 13 papers, 2 papers with code
MLPs Compass: What is learned when MLPs are combined with PLMs?
While Transformer-based pre-trained language models and their variants exhibit strong semantic representation capabilities, the question of comprehending the information gain derived from the additional components of PLMs remains an open question in this field.
Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance
Document-level event argument extraction poses new challenges of long input and cross-sentence inference compared to its sentence-level counterpart.
Ranked #1 on
Event Argument Extraction
on WikiEvents
(F1 metric)
Temporal-Coded Spiking Neural Networks with Dynamic Firing Threshold: Learning with Event-Driven Backpropagation
As a temporal encoding scheme for SNNs, Time-To-First-Spike (TTFS) encodes information using the timing of a single spike, which allows spiking neurons to transmit information through sparse spike trains and results in lower power consumption and higher computational efficiency compared to traditional rate-based encoding counterparts.
Improving Image Captioning with Control Signal of Sentence Quality
In the dataset of image captioning, each image is aligned with several descriptions.
Double Thompson Sampling in Finite stochastic Games
This algorithm achieves a total regret bound of $\tilde{\mathcal{O}}(D\sqrt{SAT})$in time horizon $T$ with $S$ states, $A$ actions and diameter $D$.
Self-Annotated Training for Controllable Image Captioning
In this paper, we propose a novel reinforcement training method for structure-related control signals: Self-Annotated Training (SAT), to improve both the accuracy and controllability of CIC models.
DPGNN: Dual-Perception Graph Neural Network for Representation Learning
To address these drawbacks, we present a novel message-passing paradigm, based on the properties of multi-step message source, node-specific message output, and multi-space message interaction.
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model
Many downstream tasks and human readers rely on the output of the ASR system; therefore, errors introduced by the speaker and ASR system alike will be propagated to the next task in the pipeline.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Improving Zero-shot Neural Machine Translation on Language-specific Encoders-Decoders
However, the performance of using multiple encoders and decoders on zero-shot translation still lags behind universal NMT.
Macroscopic Control of Text Generation for Image Captioning
With such a control signal, the controllability and diversity of existing captioning models are enhanced.
KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi
Recent progress in text classification has been focused on high-resource languages such as English and Chinese.
Improving Readability for Automatic Speech Recognition Transcription
In this work, we propose a novel NLP task called ASR post-processing for readability (APR) that aims to transform the noisy ASR output into a readable text for humans and downstream tasks while maintaining the semantic meaning of the speaker.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks
In STDBP algorithm, the timing of individual spikes is used to convey information (temporal coding), and learning (back-propagation) is performed based on spike timing in an event-driven manner.
