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Found 16 papers, 4 papers with code
Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI
Self-learning paradigms in large-scale conversational AI agents tend to leverage user feedback in bridging between what they say and what they mean.
A Vocabulary-Free Multilingual Neural Tokenizer for End-to-End Task Learning
Additionally, the dependency on a fixed vocabulary limits the subword models' adaptability across languages and domains.
CALCS 2021 Shared Task: Machine Translation for Code-Switched Data
For the unsupervised setting, we provide the following language pairs: English and Spanish-English (Eng-Spanglish), and English and Modern Standard Arabic-Egyptian Arabic (Eng-MSAEA) in both directions.
Data Augmentation for Cross-Domain Named Entity Recognition
Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models.
Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality
To alleviate these challenges, we propose a character-based subword module (char2subword) that learns the subword embedding table in pre-trained models like BERT.
Can images help recognize entities? A study of the role of images for Multimodal NER
Multimodal named entity recognition (MNER) requires to bridge the gap between language understanding and visual context.
SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets
In this paper, we present the results of the SemEval-2020 Task 9 on Sentiment Analysis of Code-Mixed Tweets (SentiMix 2020).
LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation
To facilitate research in this direction, we propose a centralized benchmark for Linguistic Code-switching Evaluation (LinCE) that combines ten corpora covering four different code-switched language pairs (i. e., Spanish-English, Nepali-English, Hindi-English, and Modern Standard Arabic-Egyptian Arabic) and four tasks (i. e., language identification, named entity recognition, part-of-speech tagging, and sentiment analysis).
Knowledge Distillation from Internal Representations
In this paper, we propose to distill the internal representations of a large model such as BERT into a simplified version of it.
Dependency-Aware Named Entity Recognition with Relative and Global Attentions
On the other hand, the global attention spots the most relevant words in the sequence.
From English to Code-Switching: Transfer Learning with Strong Morphological Clues
We show the effectiveness of this transfer learning step by outperforming multilingual BERT and homologous CS-unaware ELMo models and establishing a new state of the art in CS tasks, such as NER and POS tagging.
Multi-view Story Characterization from Movie Plot Synopses and Reviews
This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies.
Multimodal and Multi-view Models for Emotion Recognition
Studies on emotion recognition (ER) show that combining lexical and acoustic information results in more robust and accurate models.
A Multi-task Approach for Named Entity Recognition in Social Media Data
Named Entity Recognition for social media data is challenging because of its inherent noisiness.
Ranked #21 on
Named Entity Recognition (NER)
on WNUT 2017
Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task
In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data.
Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media
Our systems outperform the current F1 scores of the state of the art on the Workshop on Noisy User-generated Text 2017 dataset by 2. 45% and 3. 69%, establishing a more suitable approach for social media environments.
Ranked #17 on
Named Entity Recognition (NER)
on WNUT 2017
