Search Results for author:
Found 15 papers, 8 papers with code
Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling
Specifically, we design 5-type templates as instructional prompts, and each template includes a question that acts as the driver to teach UGEN to grasp the paradigm, options that list the candidate intents or slots to reduce the answer search space, and the context denotes original utterance.
TLM: Token-Level Masking for Transformers
Structured dropout approaches, such as attention dropout and DropHead, have been investigated to regularize the multi-head attention mechanism in Transformers.
Programming Knowledge Tracing: A Comprehensive Dataset and A New Model
More specifically, we construct a bipartite graph for programming problem embedding, and design an improved pre-training model PLCodeBERT for code embedding, as well as a double-sequence RNN model with exponential decay attention for effective feature fusion.
MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers
Over the last few years, there are a growing number of datasets and deep learning-based methods proposed for effectively solving MWPs.
Ranked #8 on
Math Word Problem Solving
on Math23K
Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification
Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieved state-of-the-art performance.
Enhancing Balanced Graph Edge Partition with Effective Local Search
The graph edge partition problem, which is to split the edge set into multiple balanced parts to minimize the total number of copied vertices, has been widely studied from the view of optimization and algorithms.
Template-based math word problem solvers with recursive neural networks
Then, we design a recursive neural network to encode the quantity with Bi-LSTM and self attention, and infer the unknown operator nodes in a bottom-up manner.
One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial Network
We evaluate our framework on {four} public benchmark datasets, all of which show that our method is superior to the other state-of-the-art methods on the tasks of object recognition and image retrieval.
Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions
Several deep learning models have been proposed for solving math word problems (MWPs) automatically.
Ranked #13 on
Math Word Problem Solving
on Math23K
Translating a Math Word Problem to an Expression Tree
Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving.
Translating a Math Word Problem to a Expression Tree
Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving.
The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers
Solving mathematical word problems (MWPs) automatically is challenging, primarily due to the semantic gap between human-readable words and machine-understandable logics.
Towards Automatic Construction of Diverse, High-quality Image Dataset
To eliminate manual annotation, in this work, we propose a novel image dataset construction framework by employing multiple textual queries.
Matrix Tri-Factorization With Manifold Regularizations for Zero-Shot Learning
By additionally introducing manifold regularizations on visual data and semantic embeddings, the learned projection can effectively captures the geometrical manifold structure residing in both visual and semantic spaces.
Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning
Specifically, the proposed framework utilizes the temporal attention for selecting specific frames to predict the related words, while the adjusted temporal attention is for deciding whether to depend on the visual information or the language context information.
