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Found 14 papers, 8 papers with code
Machine Translation for Livonian: Catering to 20 Speakers
Livonian is one of the most endangered languages in Europe with just a tiny handful of speakers and virtually no publicly available corpora.
The University of Tokyo’s Submissions to the WAT 2020 Shared Task
The paper describes the development process of the The University of Tokyo’s NMT systems that were submitted to the WAT 2020 Document-level Business Scene Dialogue Translation sub-task.
What Food Do We Tweet about on a Rainy Day?
Food choice is a complex phenomenon shaped by factors such as taste, ambience, culture or weather.
How Masterly Are People at Playing with Their Vocabulary? Analysis of the Wordle Game for Latvian
In this paper, we describe adaptation of a simple word guessing game that occupied the hearts and minds of people around the world.
Revisiting Context Choices for Context-aware Machine Translation
One of the most popular methods for context-aware machine translation (MT) is to use separate encoders for the source sentence and context as multiple sources for one target sentence.
Fragmented and Valuable: Following Sentiment Changes in Food Tweets
We analysed sentiment and frequencies related to smell, taste and temperature expressed by food tweets in the Latvian language.
Document-aligned Japanese-English Conversation Parallel Corpus
Sentence-level (SL) machine translation (MT) has reached acceptable quality for many high-resourced languages, but not document-level (DL) MT, which is difficult to 1) train with little amount of DL data; and 2) evaluate, as the main methods and data sets focus on SL evaluation.
Designing the Business Conversation Corpus
While the progress of machine translation of written text has come far in the past several years thanks to the increasing availability of parallel corpora and corpora-based training technologies, automatic translation of spoken text and dialogues remains challenging even for modern systems.
Ranked #1 on
Machine Translation
on Business Scene Dialogue JA-EN
(using extra training data)
What Can We Learn From Almost a Decade of Food Tweets
We present the Latvian Twitter Eater Corpus - a set of tweets in the narrow domain related to food, drinks, eating and drinking.
Ranked #1 on
Sentiment Analysis
on Latvian Twitter Eater Sentiment Dataset
(using extra training data)
Impact of Corpora Quality on Neural Machine Translation
Large parallel corpora that are automatically obtained from the web, documents or elsewhere often exhibit many corrupted parts that are bound to negatively affect the quality of the systems and models that learn from these corpora.
Ranked #1 on
Machine Translation
on WMT 2018 English-Finnish
Debugging Neural Machine Translations
In this paper, we describe a tool for debugging the output and attention weights of neural machine translation (NMT) systems and for improved estimations of confidence about the output based on the attention.
Ranked #4 on
Machine Translation
on WMT 2017 Latvian-English
Paying Attention to Multi-Word Expressions in Neural Machine Translation
Processing of multi-word expressions (MWEs) is a known problem for any natural language processing task.
Confidence through Attention
Attention distributions of the generated translations are a useful bi-product of attention-based recurrent neural network translation models and can be treated as soft alignments between the input and output tokens.
Ranked #3 on
Machine Translation
on WMT 2017 Latvian-English
