Tech Review on Facebook incredible chatbot:
https://www.technologyreview.com/s/604117/facebooks-perfect-impossible-chatbot/
#lyrics #mit #nlp #qa #chatbot
Facebook is quietly trying to develop the most useful virtual assistant ever, in a project that illustrates the current limitations of artificial intelligence.https://www.technologyreview.com/s/604117/facebooks-perfect-impossible-chatbot/
#lyrics #mit #nlp #qa #chatbot
MIT Technology Review
Facebook’s Perfect, Impossible Chatbot
Facebook is quietly trying to develop the most useful virtual assistant ever, in a project that illustrates the current limitations of artificial intelligence.
The Conversational Intelligence Challenge
NIPS 2017 Live Competition
Recent advances in machine learning have sparked a renewed interest for dialogue systems in the research community. In addition to the growing real-world applications, the ability to converse is closely related to the overall goal of AI. This NIPS Live Competition aims to unify the community around the challenging task: building systems capable of intelligent conversations. Teams are expected to submit dialogue systems able to carry out intelligent and natural conversations about specific news articles with humans. At the final stage of the competition participants, as well as volunteers, will be randomly matched with a bot or a human to chat and evaluate answers of a peer. We expect the competition to have two major outcomes: (1) a measure of quality of state-of-the-art dialogue systems, and (2) an open-source dataset collected from evaluated dialogues.
Organizers
Mikhail Burtsev, Valentin Malykh, MIPT, Moscow
Ryan Lowe, McGill University, Montreal
Iulian Serban, Yoshua Bengio, University of Montreal, Montreal
Alexander Rudnicky, Alan W. Black, Carnegie Mellon University, Pittsburgh
http://convai.io
#nlp #qa #nips
NIPS 2017 Live Competition
Recent advances in machine learning have sparked a renewed interest for dialogue systems in the research community. In addition to the growing real-world applications, the ability to converse is closely related to the overall goal of AI. This NIPS Live Competition aims to unify the community around the challenging task: building systems capable of intelligent conversations. Teams are expected to submit dialogue systems able to carry out intelligent and natural conversations about specific news articles with humans. At the final stage of the competition participants, as well as volunteers, will be randomly matched with a bot or a human to chat and evaluate answers of a peer. We expect the competition to have two major outcomes: (1) a measure of quality of state-of-the-art dialogue systems, and (2) an open-source dataset collected from evaluated dialogues.
Organizers
Mikhail Burtsev, Valentin Malykh, MIPT, Moscow
Ryan Lowe, McGill University, Montreal
Iulian Serban, Yoshua Bengio, University of Montreal, Montreal
Alexander Rudnicky, Alan W. Black, Carnegie Mellon University, Pittsburgh
http://convai.io
#nlp #qa #nips
QUESTION ANSWER ARCHITECTURES – SQUAD 2.0 + U-NET
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
Artilce provides easy intro into NLP, covering very basic methods and defenitions.
Link: https://betterlearningforlife.com/2018/11/16/question-answer-architectures-squad-2-0-u-net/
#NLP #QA #SQuAD #novice
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
Artilce provides easy intro into NLP, covering very basic methods and defenitions.
Link: https://betterlearningforlife.com/2018/11/16/question-answer-architectures-squad-2-0-u-net/
#NLP #QA #SQuAD #novice
Google’s open source Active Question Reformulation with Reinforcement Learning
Project: https://ai.googleblog.com/2018/10/open-sourcing-active-question.html
Github: https://github.com/google/active-qa
Publication: https://ai.google/research/pubs/pub46733
#nlp #qa #google #opensource
Project: https://ai.googleblog.com/2018/10/open-sourcing-active-question.html
Github: https://github.com/google/active-qa
Publication: https://ai.google/research/pubs/pub46733
#nlp #qa #google #opensource
Googleblog
Open Sourcing Active Question Reformulation with Reinforcement Learning
OpenAI’s new model can generate surprisingly realistic fake news.
New model, called GPT-2 is an unsupervised language model that can generate coherent paragraphs and perform rudimentary reading comprehension, machine translation, question answering, and summarization — all without task-specific training.
Link: https://blog.openai.com/better-language-models/
Paper: https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
#OpenAI #NLP #fakenews #qa #DL
New model, called GPT-2 is an unsupervised language model that can generate coherent paragraphs and perform rudimentary reading comprehension, machine translation, question answering, and summarization — all without task-specific training.
Link: https://blog.openai.com/better-language-models/
Paper: https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
#OpenAI #NLP #fakenews #qa #DL
Long-form question answering
Facebook AI shared the first large-scale data set, code, and baseline models for long-form QA, which requires machines to provide long, complex answers — something that existing algorithms have not been challenged to do before.
Link: https://ai.facebook.com/blog/longform-qa/
#FacebookAI #Facebook #NLP #NLU #QA
Facebook AI shared the first large-scale data set, code, and baseline models for long-form QA, which requires machines to provide long, complex answers — something that existing algorithms have not been challenged to do before.
Link: https://ai.facebook.com/blog/longform-qa/
#FacebookAI #Facebook #NLP #NLU #QA
MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
This method involves two sequential stages:
* coarse-tuning stage using out-of-domain datasets
* multitask learning stage using a larger in-domain dataset to help model generalize better with limited data.
Also, they propose a novel multi-step attention network (MAN) as the top-level classifier for the above task.
MMM demonstrate significantly advances the SOTA on four representative
Dialogue Multiple-Choice QA datasets
paper: https://arxiv.org/abs/1910.00458
#nlp #dialog #qa
This method involves two sequential stages:
* coarse-tuning stage using out-of-domain datasets
* multitask learning stage using a larger in-domain dataset to help model generalize better with limited data.
Also, they propose a novel multi-step attention network (MAN) as the top-level classifier for the above task.
MMM demonstrate significantly advances the SOTA on four representative
Dialogue Multiple-Choice QA datasets
paper: https://arxiv.org/abs/1910.00458
#nlp #dialog #qa
New tutorial on QA task
The T5 team competed against T5 in a "pub quiz" on (context-free) questions from the TriviaQA/NQ validation sets.
Result: team got 20% right; T5 got 35%.
Colab link: https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/master/notebooks/t5-trivia.ipynb
Source: twitter
#NLU #NLP #Colab #Tutorial #QA
The T5 team competed against T5 in a "pub quiz" on (context-free) questions from the TriviaQA/NQ validation sets.
Result: team got 20% right; T5 got 35%.
Colab link: https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/master/notebooks/t5-trivia.ipynb
Source: twitter
#NLU #NLP #Colab #Tutorial #QA
TyDi QA: A Multilingual Question Answering Benchmark
it's a q&a corpus covering 11 Typologically Diverse languages: russian, english, arabic, bengali, finnish, indonesian, japanese, kiswahili, korean, telugu, thai.
the authors collected questions from people who wanted an answer but did not know the answer yet.
they showed people an interesting passage from Wikipedia written in their native language and then had them ask a question, any question, as long as it was not answered by the passage and they actually wanted to know the answer.
blog post: https://ai.googleblog.com/2020/02/tydi-qa-multilingual-question-answering.html?m=1
paper: only pdf
#nlp #qa #multilingual #data
it's a q&a corpus covering 11 Typologically Diverse languages: russian, english, arabic, bengali, finnish, indonesian, japanese, kiswahili, korean, telugu, thai.
the authors collected questions from people who wanted an answer but did not know the answer yet.
they showed people an interesting passage from Wikipedia written in their native language and then had them ask a question, any question, as long as it was not answered by the passage and they actually wanted to know the answer.
blog post: https://ai.googleblog.com/2020/02/tydi-qa-multilingual-question-answering.html?m=1
paper: only pdf
#nlp #qa #multilingual #data
