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Found 13 papers, 3 papers with code
Domain Mismatch Doesn’t Always Prevent Cross-lingual Transfer Learning
Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised machine translation, etc.
The Engage Corpus: A Social Media Dataset for Text-Based Recommender Systems
Social media platforms play an increasingly important role as forums for public discourse.
ACID: Abstractive, Content-Based IDs for Document Retrieval with Language Models
Our results demonstrate the effectiveness of human-readable, natural-language IDs in generative retrieval with LMs.
NarrowBERT: Accelerating Masked Language Model Pretraining and Inference
We propose NarrowBERT, a modified transformer encoder that increases the throughput for masked language model pretraining by more than $2\times$.
Domain Mismatch Doesn't Always Prevent Cross-Lingual Transfer Learning
Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised machine translation, etc.
Unsupervised Bitext Mining and Translation via Self-trained Contextual Embeddings
We then improve an XLM-based unsupervised neural MT system pre-trained on Wikipedia by supplementing it with pseudo-parallel text mined from the same corpus, boosting unsupervised translation performance by up to 3. 5 BLEU on the WMT'14 French-English and WMT'16 German-English tasks and outperforming the previous state-of-the-art.
The Multilingual Amazon Reviews Corpus
We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification.
Improving Non-autoregressive Neural Machine Translation with Monolingual Data
Non-autoregressive (NAR) neural machine translation is usually done via knowledge distillation from an autoregressive (AR) model.
Don't Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings
Multilingual contextual embeddings have demonstrated state-of-the-art performance in zero-shot cross-lingual transfer learning, where multilingual BERT is fine-tuned on one source language and evaluated on a different target language.
Attentional Speech Recognition Models Misbehave on Out-of-domain Utterances
We discuss the problem of echographic transcription in autoregressive sequence-to-sequence attentional architectures for automatic speech recognition, where a model produces very long sequences of repetitive outputs when presented with out-of-domain utterances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER
We report the magnitude of the improvement on the multilingual MLDoc text classification and CoNLL 2002/2003 named entity recognition tasks.
A neural interlingua for multilingual machine translation
We incorporate an explicit neural interlingua into a multilingual encoder-decoder neural machine translation (NMT) architecture.
A practical approach to dialogue response generation in closed domains
We describe a prototype dialogue response generation model for the customer service domain at Amazon.
