#rep4nlp Yulia Tsvetkov talk #4

"Modeling Output spaces of NLP models" instead of the common #Bertology that focuses on Modeling input spaces only.

#ACL2019nlp The focus in the presentation will be on Conditional language generation
#MT #summarization ..etc

Talk3: @raquelfdzrovira talking about representations shaped by dialogue interaction data.

#ACL2019nlp #rep4nlp "Task-oriented dialogue" is the setup we are discussing now because it gives us success notion to the dialogue analyse

#rep4nlp Invited talk 2: @mohitban47 "Adversaially robust Representation Learning"

#acl2019nlp @mohitban47 Adv. examples can break Reading comprehension systems.

"Adversarial Examples for Evaluating Reading Comprehension Systems" Jia and liang 2017
arxiv.org/abs/1707.07328

Marco Baroni is starting the first invited talk at #rep4nlp
"Language is representations by itself. " #ACL2019nlp @sigrep_acl @sigrep_acl Marco is talking about his previous work about the emergence of language communication between agents.

References:

"MULTI-AGENT COOPERATION
AND THE EMERGENCE OF (NATURAL) LANGUAGE"
arxiv.org/pdf/1612.07182…

"How agents see things"
aclweb.org/anthology/D18-…

#ACL2019nlp

@yuedongP et al. presenting: neural programmer interpreter model for sentence simplification. MT methods for text simplification suffer from conservatism. meaning they simply less and copy more. This mainly because the high overlap between source and target data because of monolingualism

Highres: Reference-less Evaluation of summarisation

sashy narayan, hardy and @vlachos_nl
#acl2019nlp Automatic single reference based evaluation of summarisation is biased for several reasons. human evaluation is not feasible.

ACL keynote#1 Liang Huang: Simultaneous Translation (Machine Interpretation)

One of the main reasons for latency in Simultaneous Machine Translation is the problem of Word order (e.g. German verb comes at the end) #ACL2019nlp Current solutions in industry at the moment was to translate sentence by sentence which will introduce some latency. Work in academia include methods that either anticipates the "German verb" on the source-side.
or RL to keep waiting for the german verb (Gu et al. 2017)

Tutorial 2: Story telling from structured data ans knowledge graphs #ACL2019nlp
Anirban laha @anirbanlaha Parag jain
#data2text #nlproc #NLG @anirbanlaha Motivations for #Data2Text:
* Answer display in Question Answering systems
* kB summarization
* Question Generation

#ACL2019nlp is kicking off by "Latent structure models for NLP" ~ Andre F. T. Martins Tsvetomila Mihaylova @meloncholist @vnfrombucharest

The tutorial slides can be found here: deep-spin.github.io/tutorial/acl.p…

updates here 👇👇

#ACL2019nlp @meloncholist @vnfrombucharest Andre is starting with a motivational introduction about some structured prediction tasks (POS tagging, Dependency parsing, Word alignment)