The ACL Anthology: Current State and Future Directions

• Authors: Daniel Gildea, Min-Yen Kan, Nitin Madnani, Christoph Teichmann and Martín Villalba
• Presented at: Workshop for NLP Open Source Software (NLP-OSS) (July 20, 2018)

@InProceedings{W18-2504,
  author = 	"Gildea, Daniel
		and Kan, Min-Yen
		and Madnani, Nitin
		and Teichmann, Christoph
		and Villalba, Martin",
  title = 	"The ACL Anthology: Current State and Future Directions",
  booktitle = 	"Proceedings of Workshop for NLP Open Source Software (NLP-OSS)",
  year = 	"2018",
  publisher = 	"Association for Computational Linguistics",
  pages = 	"23--28",
  location = 	"Melbourne, Australia",
  url = 	"http://aclweb.org/anthology/W18-2504"
}

The Association of Computational Linguistic's Anthology is the open source archive, and the main source for computational linguistics and natural language processing's scientific literature. The ACL Anthology is currently maintained exclusively by community volunteers and has to be available and up-to-date at all times. We first discuss the current, open source approach used to achieve this, and then discuss how the planned use of Docker images will improve the Anthology's long-term stability. This change will make it easier for researchers to utilize Anthology data for experimentation. We believe the ACL community can directly benefit from the extension-friendly architecture of the Anthology. We end by issuing an open challenge of reviewer matching we encourage the community to rally towards.

Generating Contrastive Referring Expressions

• Authors: Martín Villalba, Christoph Teichmann and Alexander Koller
• Presented at: The 55th Annual Meeting of the Association for Computational Linguistics (ACL) (August 1, 2017)

@InProceedings{P17-1063,
  author = 	"Villalba, Martin
		and Teichmann, Christoph
		and Koller, Alexander",
  title = 	"Generating Contrastive Referring Expressions",
  booktitle = 	"Proceedings of the 55th Annual Meeting of the Association for      Computational Linguistics (Volume 1: Long Papers)    ",
  year = 	"2017",
  publisher = 	"Association for Computational Linguistics",
  pages = 	"678--687",
  location = 	"Vancouver, Canada",
  doi = 	"10.18653/v1/P17-1063",
  url = 	"http://aclweb.org/anthology/P17-1063"
}

The referring expressions (REs) produced by a natural language generation (NLG) system can be misunderstood by the hearer, even when they are semantically correct. In an interactive setting, the NLG system can try to recognize such misunderstandings and correct them. We present an algorithm for generating corrective REs that use contrastive focus ("no, the BLUE button") to emphasize the information the hearer most likely misunderstood. We show empirically that these contrastive REs are preferred over REs without contrast marking.

The Impact of Listener Gaze on Predicting Reference Resolution

• Authors: Nikolina Koleva, Martín Villalba, Maria Staudte and Alexander Koller
• Presented at: The 53rd Annual Meeting of the Association for Computational Linguistics (ACL) (July 27, 2015)

@inProceedings{DBLP:dblp_conf/acl/KolevaVSK15,
   author              = {Nikolina Koleva and 
                          Martin Villalba and 
                          Maria Staudte and 
                          Alexander Koller},
   title               = {The Impact of Listener Gaze on Predicting Reference Resolution.},
   booktitle           = {ACL (2)},
   year                = {2015},
   pages               = {812-817},
   ee                  = {http://aclweb.org/anthology/P/P15/P15-2133.pdf}
}

We investigate the impact of listener's gaze on predicting reference resolution in situated interactions. We extend an existing model that predicts to which entity in the environment listeners will resolve a referring expression (RE). Our model makes use of features that capture which objects were looked at and for how long, reflecting listeners' visual behavior. We improve a probabilistic model that considers a basic set of features for monitoring listeners' movements in a virtual environment. Particularly, in complex referential scenes, where more objects next to the target are possible referents, gaze turns out to be beneficial and helps deciphering listeners' intention. We evaluate performance at several prediction times before the listener performs an action, obtaining a highly significant accuracy gain.

Predicting the resolution of referring expressions from user behavior

• Authors: Nikos Engonopoulos, Martín Villalba, Ivan Titov and Alexander Koller
• Presented at: Converence on Empirical Methods in Natural Language Processing (EMNLP) (October 19, 2013)

@InProceedings{engonopoulos:2013:EMNLP,
    author    = {Nikos Engonopoulos and Mart\'in Villalba 
		 and Ivan Titov and Alexander Koller},
    title     = {Predicting the resolution of referring
		 expressions from user behavior},
    booktitle = {Proceedings of the Conference on
		 Empirical Methods in Natural Language
		 Processing (EMNLP)},
    month     = {October},
    year      = {2013},
    address   = {Seattle, WA}
}

We present a statistical model for predicting how the user of an interactive, situated NLP system resolved a referring expression. The model makes an initial prediction based on the meaning of the utterance, and revises it continuously based on the user's behavior. The combined model outperforms its components in predicting reference resolution and when to give feedback.

Interpreting Natural Language Instructions Using Language, Vision, and Behavior

• Authors: Luciana Benotti, Tessa Lau and Martín Villalba
• Published in: ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Multiple Modalities in Interactive Systems and Robots. Volume 4 Issue 3 (October 2014)

	@article{DBLP:journals/tiis/BenottiLV14,
	  author    = {Luciana Benotti and Tessa A. Lau
			   and Mart\'in Villalba},
	  title     = {Interpreting Natural Language Instructions
			   Using Language, Vision, and Behavior},
	  journal   = {TiiS},
	  volume    = {4},
	  number    = {3},
	  pages     = {13:1--13:22},
	  year      = {2014},
	  url       = {http://doi.acm.org/10.1145/2629632},
	  doi       = {10.1145/2629632},
	  timestamp = {Wed, 29 Apr 2015 17:56:53 +0200},
	  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/tiis/BenottiLV14},
	  bibsource = {dblp computer science bibliography,
			   http://dblp.org}
	}
	

We define the problem of automatic instruction interpretation as follows. Given a natural language instruction, can we automatically predict what an instruction follower, such as a robot, should do in the environment to follow that instruction? Previous approaches to automatic instruction interpretation have required either extensive domain-dependent rule writing or extensive manually annotated corpora. This article presents a novel approach that leverages a large amount of unannotated, easy-to-collect data from humans interacting in a game-like environment. (...) Our empirical analysis shows that machine learning classifiers achieve 77% accuracy on this task on available English corpora and 74% on similar German corpora. Finally, the inclusion of human feedback in the interpretation process is shown to boost performance to 92% for the English corpus and 90% for the German corpus.

Corpus-based Interpretation of Instructions in Virtual Environments

• Authors: Luciana Benotti, Tessa Lau, Julián Cerruti and Martín Villalba
• Presented at: Proceedings of the 50th Anual Meeting of the Association for Computational Linguistics (April 23, 2012)

@inproceedings{DBLP:conf/acl/BenottiVLC12,
    author    = {Luciana Benotti and Mart\'in Villalba
		 and Tessa A. Lau and Juli\'an A. Cerruti},
    title     = {Corpus-based Interpretation of Instructions
		 in Virtual Environments},
    booktitle = {The 50th Annual Meeting of the Association
		 for Computational Linguistics,
                 Proceedings of the Conference, July 8-14,
		 2012, Jeju Island, Korea
		 - Volume 2: Short Papers},
    pages     = {181--186},
    year      = {2012},
    crossref  = {DBLP:conf/acl/2012-2},
    url       = {http://www.aclweb.org/anthology/P12-2036},
    timestamp = {Wed, 15 Aug 2012 09:10:28 +0200},
    biburl    = {http://dblp.uni-trier.de/rec/bib/conf/acl/BenottiVLC12},
    bibsource = {dblp computer science bibliography,
		 http://dblp.org}
}

Previous approaches to instruction interpretation have required either extensive domain adaptation or manually annotated corpora. This paper presents a novel approach to instruction interpretation that leverages a large amount of unannotated, easy-to-collect data from humans interacting with a virtual world. We compare several algorithms for automatically segmenting and discretizing this data into (utterance, reaction) pairs and training a classifier to predict reactions given the next utterance. Our empirical analysis shows that the best algorithm achieves 70% accuracy on this task, with no manual annotation required.

Inference of Strategic Points in Virtual Worlds (Spanish)

• Authors: Luciana Benotti and Martín Villalba
• Presented at: Argentinean workshop on videogames (WAVI) (October 21, 2011)

@unpublished{aowebreq:clei2010:dv,
    author    = {Mart\'in Villalba and Luciana Benotti},
    title     = {Inferencia de puntos estrat\'egicos en
		 mundos virtuales},
    year      = {2011},
    note      = {Workshop Argention de Videojuegos (WAVI)}
}

Strategic points are a specific kind of waypoint inside a virtual ol physical world which are vital to the successful completion of a task. This points are usually placed by hand by the world designer, leading usually to unnatural movements, and requiring the designer to do the task over and over each time the world changes.
In order to infer the location of strategic points, we've researched the movement of human players, looking for common behavior patterns; by analysing their movements and results in certain tasks, we've been able to infer both relevant strategic points and movement patterns.

Aspect-Oriented web requirements engineering with model transformations (Spanish)

• Authors: Juan Durán and Martín Villalba
• Presented at: Latin-American Conference on Informatics (CLEI) (October 20, 2010)

@unpublished{aowebreq:clei2010:dv,
    author    = {Juan Dur\'an and Mart\'in Villalba},
    title     = {Ingenier\'ia de requisitos web orientada a
		 aspectos con transformaci\'on de modelos},
    year      = {2010},
    note      = {Conferencia Latinoamericana de Inform\'atica
		 (CLEI)}
}

Analysts usually describe requirements using notations with technical concepts that clients don't understand. Expression requirements with a non-technical notation (NLC) allow the clients to understand and validate the requirements analysis process.
While there is previous work in this area, most of it was directed towards functional requirements of a system, while the area of non-functional requirements was neglected. Because of this situation, we propose a graphical notation (readable by non-techical users and oriented towards information systems) to allow analysts to express how non-functional aspects of their systems affect, on a global scale, the functional aspects of a system.
As the whole process is based on models, we also present an implementation of a model transformation in ATL to transform two well known requirement models into aspect-oriented requirements models.

Bringing your NLP research to the World: Good practices on sharing your research with end-users (LatinX Social @ NAACL 2021)

The LatinX Social event took place in June 2021, organized by LXAI during NAACL 2021. I presented these slides about practical advice on how to share your code with future you, your colleagues, programmers, and end users.