Search Results for author:
Found 22 papers, 7 papers with code
A Unified Encoding of Structures in Transition Systems
Transition systems usually contain various dynamic structures (e. g., stacks, buffers).
Word Reordering for Zero-shot Cross-lingual Structured Prediction
Adapting word order from one language to another is a key problem in cross-lingual structured prediction.
EnTDA: Entity-to-Text based Data Augmentation Approach for Named Entity Recognition Tasks
To alleviate the excessive reliance on the dependency order among entities in existing augmentation paradigms, we develop an entity-to-text instead of text-to-entity based data augmentation method named: EnTDA to decouple the dependencies between entities by adding, deleting, replacing and swapping entities, and adopt these augmented data to bootstrap the generalization ability of the NER model.
Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation
Existing zero-shot methods fail to align the two modalities of speech and text into a shared semantic space, resulting in much worse performance compared to the supervised ST methods.
ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition
As text representations take the most important role in MNER, in this paper, we propose {\bf I}mage-{\bf t}ext {\bf A}lignments (ITA) to align image features into the textual space, so that the attention mechanism in transformer-based pretrained textual embeddings can be better utilized.
Ranked #1 on
Multi-modal Named Entity Recognition
on Twitter-17
Multi-modal Named Entity Recognition
named-entity-recognition
MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations
Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing.
Ranked #1 on
Entity Retrieval
on ZESHEL
Risk Minimization for Zero-shot Sequence Labeling
In this paper, we propose a novel unified framework for zero-shot sequence labeling with minimum risk training and design a new decomposable risk function that models the relations between the predicted labels from the source models and the true labels.
Multi-View Cross-Lingual Structured Prediction with Minimum Supervision
In structured prediction problems, cross-lingual transfer learning is an efficient way to train quality models for low-resource languages, and further improvement can be obtained by learning from multiple source languages.
Bridging the Domain Gap: Improve Informal Language Translation via Counterfactual Domain Adaptation
Despite the near-human performances already achieved on formal texts such as news articles, neural machine transla- tion still has difficulty in dealing with ”user-generated” texts that have diverse linguistic phenomena but lack large-scale high-quality parallel corpora.
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
We find empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence.
Ranked #1 on
Named Entity Recognition
on CMeEE
An Investigation of Potential Function Designs for Neural CRF
The neural linear-chain CRF model is one of the most widely-used approach to sequence labeling.
Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor
The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student's output distributions.
Automated Concatenation of Embeddings for Structured Prediction
Pretrained contextualized embeddings are powerful word representations for structured prediction tasks.
Ranked #1 on
Part-Of-Speech Tagging
on ARK
More Embeddings, Better Sequence Labelers?
Recent work proposes a family of contextual embeddings that significantly improves the accuracy of sequence labelers over non-contextual embeddings.
Ranked #2 on
Chunking
on CoNLL 2003 (German)
AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network
The linear-chain Conditional Random Field (CRF) model is one of the most widely-used neural sequence labeling approaches.
Ranked #3 on
Chunking
on CoNLL 2003 (German)
Weakly Supervised Attentional Model for Low Resource Ad-hoc Cross-lingual Information Retrieval
We propose a weakly supervised neural model for Ad-hoc Cross-lingual Information Retrieval (CLIR) from low-resource languages.
Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages
Automatic speech recognition (ASR) systems often need to be developed for extremely low-resource languages to serve end-uses such as audio content categorization and search.
Transfer Learning based Dynamic Multiobjective Optimization Algorithms
This approach takes the transfer learning method as a tool to help reuse the past experience for speeding up the evolutionary process, and at the same time, any population based multiobjective algorithms can benefit from this integration without any extensive modifications.
Statistical Machine Translation Features with Multitask Tensor Networks
We present a three-pronged approach to improving Statistical Machine Translation (SMT), building on recent success in the application of neural networks to SMT.
