Natural Language Processing has made significant strides in a number of applications such as Machine Translation, Question Answering, and Text Classification. NLP applications, however, still have difficulty dealing with “non-standard” text. For example, recently Google Translate was reported to “hallucinate” religious prophecies when typing “dog” 19 times . Compared to traditional sources such as news corpora, the user-generated text brings a set of unique challenges. These include the use of informal language (e.g. hashtags and slang) as well as a long tail of variations such as misspellings, typos and creative use of language. We need to teach NLP systems to deal with these diverse linguistic phenomena in order to perform well under typical operating conditions .
In the specific case of neural machine translation (NMT), recent work has claimed to achieve results near-human performance [3, 10]. These results cover translations on formal domains such as news articles. When they are evaluated under noisy or adversarial conditions, these state-of-the-art systems often fail [6, 11], which suggests there exists intrinsic weakness of seq2seq models [12, 13]. In order to use translation to facilitate conversations that cross language barriers, we need to address these challenges. Examples, where translation systems can be used successfully for informal text, include messaging applications (Messenger, Whatsapp, iMessage), content sharing on social media (Facebook, Instagram, Twitter), and discussion forums (Reddit) .
We will focus on developing machine translation systems that address the following research questions.
We will research new methods to address these challenges during the workshop. This will require modeling and algorithmic expertise in building encoders that are robust to language variations and building decoders that can condition on context. Building relevant datasets prior to the workshop will require crowdsourcing and annotation expertise to obtain translations that capture the social meaning of the original text. Finally, dialogue modeling expertise will allow to track changes in dialogue state and determine how they might affect the translation.
As part of the workshop, we expect to deliver: (i) novel methods for dealing with informal, noisy text to produce more accurate translations and (ii) novel methods to utilize context when translating, specifically when translating an informal conversational text.