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Found 16 papers, 14 papers with code
When Does Monolingual Data Help Multilingual Translation: The Role of Domain and Model Scale
Next, we analyze the impact of scale (from 90M to 1. 6B parameters) and find it is important for both methods, particularly DAE.
Automatic Evaluation and Analysis of Idioms in Neural Machine Translation
A major open problem in neural machine translation (NMT) is the translation of idiomatic expressions, such as "under the weather".
Multilingual Machine Translation with Hyper-Adapters
We find that hyper-adapters are more parameter efficient than regular adapters, reaching the same performance with up to 12 times less parameters.
Exploring Unsupervised Pretraining Objectives for Machine Translation
Unsupervised cross-lingual pretraining has achieved strong results in neural machine translation (NMT), by drastically reducing the need for large parallel data.
Language Model Prior for Low-Resource Neural Machine Translation
A common solution is to exploit the knowledge of language models (LM) trained on abundant monolingual data.
Attention-based Conditioning Methods for External Knowledge Integration
This form of conditioning on the attention distribution, enforces the contribution of the most salient words for the task at hand.
SEQ\^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression
The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets.
SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression
The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets.
An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models
A growing number of state-of-the-art transfer learning methods employ language models pretrained on large generic corpora.
Integrating Recurrence Dynamics for Speech Emotion Recognition
We investigate the performance of features that can capture nonlinear recurrence dynamics embedded in the speech signal for the task of Speech Emotion Recognition (SER).
Ranked #46 on
Emotion Recognition in Conversation
on IEMOCAP
Emotion Recognition in Conversation
Speech Emotion Recognition
NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification
In this paper we present our approach to tackle the Implicit Emotion Shared Task (IEST) organized as part of WASSA 2018 at EMNLP 2018.
NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs
In this paper we present two deep-learning systems that competed at SemEval-2018 Task 3 "Irony detection in English tweets".
NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention
In this paper we present a deep-learning model that competed at SemEval-2018 Task 2 "Multilingual Emoji Prediction".
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning
In this paper we present deep-learning models that submitted to the SemEval-2018 Task~1 competition: "Affect in Tweets".
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis
Both the word embeddings and our text processing tool are available to the research community.
Ranked #2 on
Sentiment Analysis
on SemEval 2017 Task 4-A
DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison
In this paper we present a deep-learning system that competed at SemEval-2017 Task 6 ''{\#}HashtagWars: Learning a Sense of Humor{''}.
