Link: http://acl2015.org/accepted_papers.html
Here is my subjective list of remarkable papers relating to MT research:
*** Conventional Statistical Machine Translation
A CONTEXT-AWARE TOPIC MODEL FOR STATISTICAL MACHINE TRANSLATION
Jinsong Su, Deyi Xiong, Yang Liu, Xianpei Han, Hongyu Lin and Junfeng Yao
NON-LINEAR LEARNING FOR STATISTICAL MACHINE TRANSLATION
Shujian Huang, Huadong Chen, Xinyu Dai and Jiajun Chen
MULTI-TASK LEARNING FOR MULTIPLE LANGUAGE TRANSLATION
Daxiang Dong, Hua Wu, Wei He, Dianhai Yu and Haifeng Wang
WHAT’S IN A DOMAIN? ANALYZING GENRE AND TOPIC DIFFERENCES IN STATISTICAL MACHINE TRANSLATION
Marlies van der Wees, Arianna Bisazza, Wouter Weerkamp and Christof Monz
*** Neural Machine Translation
ADDRESSING THE RARE WORD PROBLEM IN NEURAL MACHINE TRANSLATION
Thang Luong, Ilya Sutskever, Quoc Le, Oriol Vinyals and Wojciech Zaremba
ENCODING SOURCE LANGUAGE WITH CONVOLUTIONAL NEURAL NETWORK FOR MACHINE TRANSLATION
Fandong Meng, Zhengdong Lu, Mingxuan Wang, Hang Li, Wenbin Jiang and Qun Liu
IMPROVED NEURAL NETWORK FEATURES, ARCHITECTURE AND LEARNING FOR STATISTICAL MACHINE TRANSLATION
Hendra Setiawan, Zhongqiang Huang, Jacob Devlin, Thomas Lamar and Rabih Zbib
NON-PROJECTIVE DEPENDENCY-BASED PRE-REORDERING WITH RECURRENT NEURAL NETWORK FOR MACHINE TRANSLATION
Antonio Valerio Miceli Barone
ON USING VERY LARGE TARGET VOCABULARY FOR NEURAL MACHINE TRANSLATION
Sebastien Jean, Kyunghyun Cho, Roland Memisevic and Yoshua Bengio
CONTEXT-DEPENDENT TRANSLATION SELECTION USING CONVOLUTIONAL NEURAL NETWORK
Baotian Hu, Zhaopeng Tu, Zhengdong Lu and Hang Li
*** Machine Translation Evaluation and Quality Estimation
ONLINE MULTITASK LEARNING FOR MACHINE TRANSLATION QUALITY ESTIMATION
José G. C. de Souza, Matteo Negri, Marco Turchi and Elisa Ricci
PAIRWISE NEURAL MACHINE TRANSLATION EVALUATION
Francisco Guzmán, Shafiq Joty, Lluís Màrquez and Preslav Nakov
EVALUATING MACHINE TRANSLATION SYSTEMS WITH SECOND LANGUAGE PROFICIENCY TESTS
Takuya Matsuzaki, Akira Fujita, Naoya Todo and Noriko H. Arai
Some notes:
*** According to my observation, there are some research trends depending on data characteristics:
- very big data
- heterogeneous data
- multi-lingual data
*** And of course, deep learning research is still very hot.
Showing posts with label ACL. Show all posts
Showing posts with label ACL. Show all posts
Saturday, 4 July 2015
Wednesday, 11 July 2012
Interested Papers at ACL 2012
Sunday, 8 July 2012
Saffron - Extracting the Valuable Threads of Expertise
Link: http://saffron.deri.ie/acl
Intro: Saffron provides insights in a research community or organization by analysing its main topics of investigation and the experts associated with these topics.
Saffron analysis is fully automatic and is based on text mining and linked data principles.
This instance of Saffron analyzes the research community in Natural Language Processing based on the proceedings of the conferences organized by the Association for Computational Linguistics (ACL).
Intro: Saffron provides insights in a research community or organization by analysing its main topics of investigation and the experts associated with these topics.
Saffron analysis is fully automatic and is based on text mining and linked data principles.
This instance of Saffron analyzes the research community in Natural Language Processing based on the proceedings of the conferences organized by the Association for Computational Linguistics (ACL).
Sunday, 2 August 2009
ACL-IJCNLP'09 participation
Day 1 - 02/08/2009
Tutorial 1: Topics in Statistical Machine Translation by Kevin Knight (ISI) and Philippe Koehn (Edinburgh Univ.)
Some sub-topics within this tutorial I need to be take into account are as follows:
- Minimum Bayesian Risk Decoding
- Re-evaluation of phrase-based SMT outputs
- MT system combination
- Efficient decoding (e.g. using cube pruning)
- Discriminative training with various features
I am looking for their slides (soft) for my further reference. If you have it, please share with me, thanks a lot!
Day 2 - 03/08/2009
Session 2B: Generation and Summarization 1
Talk 1: DEPEVAL (summ): Dependency-based Evaluation for Automatic Summaries by Karolina Owczarzakek
- the main idea is to use dependency relations to summary evaluation
- better in comparison with ROUGE (2004) and BE (2005)
+ Question: difference between DEPEVAL and BE?
Note:
- Lexical-Functional Grammar (e.g. two syntactic structures to one functional structure)
- LFG parser
+ Charniak-Johnson syntactic parser (2005)
+ LFG annotation (2008)
Talk 2: Summarizing Definition from Wikipedia by Shiren Ye
- raise new problem with their challenges in summarization of Wikipedia articles
+ recursive links
+ hidden information
- single-document summarization
- use existing approach named Document Concept Lattice (IPM 2007)
Talk 3: Automatically Generating Wikipedia Articles: A Structure-aware Approach by C. Sauper
- new problem in generating overview articles in Wikipedia using various resources crawled from the Internet
- template creation using clustering existing section topics in database
- proposed joint learning model that integrates Integer Linear Programming (ILP) into learning to optimize weights (for each section topic)
Note:
- evaluation of quality of generated articles is subjective (Prof. Hovy asked about this)!
Talk 4: Learning to tell tales: A Data-driven Approach to Story Generation by Neil McIntyre
- an interesting problem that results end-to-end generation system
- content selection (content) -> content planning (grammar) -> generation (use LM)
Note:
- how to evaluate the quality of generated stories in terms of coherence and interestingness?
Day 3 - 04/08/2009
Relax to save my energy to enjoy the interesting remaining sessions, especially in EMNLP!
Day 4 - 05/08/2009
Talk 1: SMS based Interface for FAQ Retrieval
- actually cannot follow the Indian guy who is speaker of this talk.
Talk 2: A Syntax-free Approach to Japanese Sentence Compression
- It is worthy noting some materials relevant to my current interest as follows:
+Intra-sentence positional term weighting
+Patched language modeling
- Analysis of human-made reference compression
-> very helpful to figure out challenges in specific problems!
- combinatorial optimization problem
-> used to do parameter optimization (MCE-Minimum Classification Error in this paper)
- Statistical significance using Wilcoxin sign T-test
Talk 3: Application-driven Statistical Paraphrase Generation
- use SMT-like techniques but propose some new models within noisy channel model
+ paraphrase model (adapt)
+ LM (re-use)
+ usability (propose)
- seems to be not compelling about error analysis (only exhibit the very good outputs of proposed system), and figure out which components in proposed framework are most influential?
Talk 4: Word or Phrase? Learning Which Unit to Stress for Information Retrieval
It seems to not interest me a lot, IR stuffs!
Talk 5: A Generative Blog Post Retrieval Model that Uses Query Expansion based on External Collections
- Learn about query expansion techniques and how to integrate it into specific problem (blog post retrieval in this paper)
+ worthy noting query expansion based on external resources
Talk 6: An Optimal-Time Binarization Algorithm for Linear Context-Free Rewriting Systems with Fan-Out Two
- a lot of parsing-relevant stuffs (especially in algorithm complexity) in this talks that made me extremely confused!
Talk 7: A Polynominal-Time Parsing Algorithm for TT-MCTAG
- cannot understand any materials!
Talk 8: Unsupervised Relation Extraction by Mining Wikipedia Texts Using Information from the Web
- quite simple idea (just in my opinion) based on observation from Wikipedia
- use dependency as key components
Closing session:
- very interesting and sometimes funny!
- Prof. Frederick Jelineks has been received the Lifetime Achievement Award and then following by interesting talks about his biography sketch.
- announcements about future NLP conferences (COLING'10, ACL'10, ACL'11, NAACL'10, IJCNLP'10, LREC'10)
- announcements about best paper awards (see more details in ACL-IJCNLP'09 website)
Exhausted but helpful day. Prepare for next days with ACL workshops and EMNLP sessions!
Day 5 - 06/08/2009
Talk 1 (invited talk): Query-focused Summarization Using Text-to-Text Generation: when Information Comes from Multilingual Sources by Kathleen R. McKeown
- This is the first time I have seen the face of Prof. Kathleen McKeown who was supervisor of my current supervisor (A/P Min-Yen KAN) hehe.
Some main points:
- typical approach for query-based summarization:
+ choose key sentences (word freq, position, clue words)
+ matches of query term against sentence terms
=> leads to
+ irrelevant sentences
+ sentences placed out of context -> misconceptions
<= - generate new sentences from selected phrases + fluent sentences -> disfluent sentences
+ edit references to people (focus mainly on names)
- remove irrelevant sentences using sentence simplification
+ project DARPA GALE
+ interactive question user input
- NIGHTINGALE
+ use Wikipedia to expand query
+ consider name translation in multilingual resources
+ better if operating over phrases
- GLARF parser from NYU
- long sentences -> shorter sentences using sentence simplification
- redundancy detection => pairwise similarity across all sentences to identify concepts
+ alignment of dependency parses --> hypergraph
+ BOW
- future research direction: text generation for QA
Talk 2: A Classification Algorithm for Predicting the Structure of Summaries
- Interesting motivated question: how to "paste" selected sentences during abstracting?
- abstracting
+ some of materials not present
+ be modeled by cut-and-paste operations (Mani, 01)
- use specific verbs (predicates), for example: present, conclude, include, ...
- language tools
+ GATE (POS, morpho)
+ SUPPLE parser
Talk 3: Entity Extraction via Ensemble Semantics
- web -> entities -> top-PMI (point-wise mutual information) entities
Talk 4: Clustering to Find Exemplar Terms for Keyphrase Extraction
- relatedness
+ co-occurrence (statistics)
+ Wikipedia-based (e.g. PMI)
Day 6 - 07/08/2009
TBA
Just for taking notes!
--
Cheers,
Vu
Tutorial 1: Topics in Statistical Machine Translation by Kevin Knight (ISI) and Philippe Koehn (Edinburgh Univ.)
Some sub-topics within this tutorial I need to be take into account are as follows:
- Minimum Bayesian Risk Decoding
- Re-evaluation of phrase-based SMT outputs
- MT system combination
- Efficient decoding (e.g. using cube pruning)
- Discriminative training with various features
I am looking for their slides (soft) for my further reference. If you have it, please share with me, thanks a lot!
Day 2 - 03/08/2009
Session 2B: Generation and Summarization 1
Talk 1: DEPEVAL (summ): Dependency-based Evaluation for Automatic Summaries by Karolina Owczarzakek
- the main idea is to use dependency relations to summary evaluation
- better in comparison with ROUGE (2004) and BE (2005)
+ Question: difference between DEPEVAL and BE?
Note:
- Lexical-Functional Grammar (e.g. two syntactic structures to one functional structure)
- LFG parser
+ Charniak-Johnson syntactic parser (2005)
+ LFG annotation (2008)
Talk 2: Summarizing Definition from Wikipedia by Shiren Ye
- raise new problem with their challenges in summarization of Wikipedia articles
+ recursive links
+ hidden information
- single-document summarization
- use existing approach named Document Concept Lattice (IPM 2007)
Talk 3: Automatically Generating Wikipedia Articles: A Structure-aware Approach by C. Sauper
- new problem in generating overview articles in Wikipedia using various resources crawled from the Internet
- template creation using clustering existing section topics in database
- proposed joint learning model that integrates Integer Linear Programming (ILP) into learning to optimize weights (for each section topic)
Note:
- evaluation of quality of generated articles is subjective (Prof. Hovy asked about this)!
Talk 4: Learning to tell tales: A Data-driven Approach to Story Generation by Neil McIntyre
- an interesting problem that results end-to-end generation system
- content selection (content) -> content planning (grammar) -> generation (use LM)
Note:
- how to evaluate the quality of generated stories in terms of coherence and interestingness?
Day 3 - 04/08/2009
Relax to save my energy to enjoy the interesting remaining sessions, especially in EMNLP!
Day 4 - 05/08/2009
Talk 1: SMS based Interface for FAQ Retrieval
- actually cannot follow the Indian guy who is speaker of this talk.
Talk 2: A Syntax-free Approach to Japanese Sentence Compression
- It is worthy noting some materials relevant to my current interest as follows:
+Intra-sentence positional term weighting
+Patched language modeling
- Analysis of human-made reference compression
-> very helpful to figure out challenges in specific problems!
- combinatorial optimization problem
-> used to do parameter optimization (MCE-Minimum Classification Error in this paper)
- Statistical significance using Wilcoxin sign T-test
Talk 3: Application-driven Statistical Paraphrase Generation
- use SMT-like techniques but propose some new models within noisy channel model
+ paraphrase model (adapt)
+ LM (re-use)
+ usability (propose)
- seems to be not compelling about error analysis (only exhibit the very good outputs of proposed system), and figure out which components in proposed framework are most influential?
Talk 4: Word or Phrase? Learning Which Unit to Stress for Information Retrieval
It seems to not interest me a lot, IR stuffs!
Talk 5: A Generative Blog Post Retrieval Model that Uses Query Expansion based on External Collections
- Learn about query expansion techniques and how to integrate it into specific problem (blog post retrieval in this paper)
+ worthy noting query expansion based on external resources
Talk 6: An Optimal-Time Binarization Algorithm for Linear Context-Free Rewriting Systems with Fan-Out Two
- a lot of parsing-relevant stuffs (especially in algorithm complexity) in this talks that made me extremely confused!
Talk 7: A Polynominal-Time Parsing Algorithm for TT-MCTAG
- cannot understand any materials!
Talk 8: Unsupervised Relation Extraction by Mining Wikipedia Texts Using Information from the Web
- quite simple idea (just in my opinion) based on observation from Wikipedia
- use dependency as key components
Closing session:
- very interesting and sometimes funny!
- Prof. Frederick Jelineks has been received the Lifetime Achievement Award and then following by interesting talks about his biography sketch.
- announcements about future NLP conferences (COLING'10, ACL'10, ACL'11, NAACL'10, IJCNLP'10, LREC'10)
- announcements about best paper awards (see more details in ACL-IJCNLP'09 website)
Exhausted but helpful day. Prepare for next days with ACL workshops and EMNLP sessions!
Day 5 - 06/08/2009
Talk 1 (invited talk): Query-focused Summarization Using Text-to-Text Generation: when Information Comes from Multilingual Sources by Kathleen R. McKeown
- This is the first time I have seen the face of Prof. Kathleen McKeown who was supervisor of my current supervisor (A/P Min-Yen KAN) hehe.
Some main points:
- typical approach for query-based summarization:
+ choose key sentences (word freq, position, clue words)
+ matches of query term against sentence terms
=> leads to
+ irrelevant sentences
+ sentences placed out of context -> misconceptions
<= - generate new sentences from selected phrases + fluent sentences -> disfluent sentences
+ edit references to people (focus mainly on names)
- remove irrelevant sentences using sentence simplification
+ project DARPA GALE
+ interactive question user input
- NIGHTINGALE
+ use Wikipedia to expand query
+ consider name translation in multilingual resources
+ better if operating over phrases
- GLARF parser from NYU
- long sentences -> shorter sentences using sentence simplification
- redundancy detection => pairwise similarity across all sentences to identify concepts
+ alignment of dependency parses --> hypergraph
+ BOW
- future research direction: text generation for QA
Talk 2: A Classification Algorithm for Predicting the Structure of Summaries
- Interesting motivated question: how to "paste" selected sentences during abstracting?
- abstracting
+ some of materials not present
+ be modeled by cut-and-paste operations (Mani, 01)
- use specific verbs (predicates), for example: present, conclude, include, ...
- language tools
+ GATE (POS, morpho)
+ SUPPLE parser
Talk 3: Entity Extraction via Ensemble Semantics
- web -> entities -> top-PMI (point-wise mutual information) entities
Talk 4: Clustering to Find Exemplar Terms for Keyphrase Extraction
- relatedness
+ co-occurrence (statistics)
+ Wikipedia-based (e.g. PMI)
Day 6 - 07/08/2009
TBA
Just for taking notes!
--
Cheers,
Vu
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