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Found 15 papers, 5 papers with code
mixSeq: A Simple Data Augmentation Methodfor Neural Machine Translation
Data augmentation, which refers to manipulating the inputs (e. g., adding random noise, masking specific parts) to enlarge the dataset, has been widely adopted in machine learning.
TDeLTA: A Light-weight and Robust Table Detection Method based on Learning Text Arrangement
The diversity of tables makes table detection a great challenge, leading to existing models becoming more tedious and complex.
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
SCB-dataset: A Dataset for Detecting Student Classroom Behavior
The use of deep learning methods for automatic detection of students' classroom behavior is a promising approach to analyze their class performance and enhance teaching effectiveness.
Structure-Unified M-Tree Coding Solver for MathWord Problem
As one of the challenging NLP tasks, designing math word problem (MWP) solvers has attracted increasing research attention for the past few years.
Tailoring Molecules for Protein Pockets: a Transformer-based Generative Solution for Structured-based Drug Design
Structure-based drug design is drawing growing attentions in computer-aided drug discovery.
Discovering Drug-Target Interaction Knowledge from Biomedical Literature
We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations.
Contextual Domain Classification with Temporal Representations
In commercial dialogue systems, the Spoken Language Understanding (SLU) component tends to have numerous domains thus context is needed to help resolve ambiguities.
CN-HIT-IT.NLP at SemEval-2020 Task 4: Enhanced Language Representation with Multiple Knowledge Triples
For this task, it is obvious that external knowledge, such as Knowledge graph, can help the model understand commonsense in natural language statements.
Learning to Reweight with Deep Interactions
Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc.
Multi-branch Attentive Transformer
While the multi-branch architecture is one of the key ingredients to the success of computer vision tasks, it has not been well investigated in natural language processing, especially sequence learning tasks.
Ranked #4 on
Machine Translation
on WMT2014 English-German
(SacreBLEU metric)
Microsoft Research Asia's Systems for WMT19
We Microsoft Research Asia made submissions to 11 language directions in the WMT19 news translation tasks.
Learning to Teach with Dynamic Loss Functions
Different from typical learning settings in which the loss function of a machine learning model is predefined and fixed, in our framework, the loss function of a machine learning model (we call it student) is defined by another machine learning model (we call it teacher).
Learning to Teach
Teaching plays a very important role in our society, by spreading human knowledge and educating our next generations.
