To study the operational behaviour of λ-terms, we will use the denotational (mathematical) approach. A denotational semantics for a language is based on the choice of a space of semantics values, or denotations, where terms are to be interpreted. Choosing a space with nice mathematical properties can help in proving the semantic properties of terms, since to this aim standard mathematical techniques can be used.

λ Δ -Models

We present an update to UDPipe 1.0 (Straka et al., 2016), a trainable pipeline which performs sentence segmentation,
tokenization, POS tagging, lemmatization and dependency parsing.
We provide models for all 50 languages of UD 2.0, and
furthermore, the pipeline can be trained easily using data in CoNLL-U format. For the purpose of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, the updated UDPipe 1.1 was used as one of the baseline systems, finishing as the 13th system of 33 participants. A further improved UDPipe 1.2 participated in the shared task, placing as the 8th best system, while achieving low running times and moder-
ately sized models.
The tool is available under open-source Mozilla Public Licence (MPL) and provides bindings for C++, Python (through
ufal.udpipe PyPI package), Perl (through UFAL::UDPipe CPAN
package), Java and C#.

/pdf/tokenizing-pos-tagging-lemmatizing-and-parsing-ud-2-0-with-4ohfqa4ope.pdf

Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe

Thispaper overviews 18 plagiarism detectors that have been developed and evaluated within PAN'10. We start with a unified retrieval process that sum- marizes the best practices employed this year. Then, the detectors' performances are evaluated in detail, highlighting several important aspects of plagiarism de- tection, such as obfuscation, intrinsic vs. external plagiarism, and plagiarism case length. Finally, all results are compared to those of last year's competition.

/pdf/overview-of-the-2nd-international-competition-on-plagiarism-30hy5ipx0c.pdf

Overview of the 2nd International Competition on Plagiarism Detection

Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated language (e.g. switching topic or sentiment) is difficult without modifying the model architecture or fine-tuning on attribute-specific data and entailing the significant cost of retraining. We propose a simple alternative: the Plug and Play Language Model (PPLM) for controllable language generation, which combines a pretrained LM with one or more simple attribute classifiers that guide text generation without any further training of the LM. In the canonical scenario we present, the attribute models are simple classifiers consisting of a user-specified bag of words or a single learned layer with 100,000 times fewer parameters than the LM. Sampling entails a forward and backward pass in which gradients from the attribute model push the LM's hidden activations and thus guide the generation. Model samples demonstrate control over a range of topics and sentiment styles, and extensive automated and human annotated evaluations show attribute alignment and fluency. PPLMs are flexible in that any combination of differentiable attribute models may be used to steer text generation, which will allow for diverse and creative applications beyond the examples given in this paper.

Plug and Play Language Models: A Simple Approach to Controlled Text Generation

The PAN task on author profiling has been organised in the
framework of the WIQ-EI IRSES project (Grant No. 269180) within the FP 7 Marie
Curie People Framework of the European Commission. We would like to thank Atribus
by Corex for sponsoring the award for the winner team. We thank Julio Gonzalo,
Jorge Carrillo and Damiano Spina from UNED for helping with the Twitter subcorpus.
The work of the first author was partially funded by Autoritas Consulting SA and
by Ministerio de Economia y Competitividad de Espana under grant ECOPORTUNITY
IPT-2012-1220-430000 and CSO2013-43054-R. The work of the second author was in
the framework the DIANA-APPLICATIONS-Finding Hidden Knowledge in Texts: Applications
(TIN2012-38603-C02-01) project, and the VLC/CAMPUS Microcluster on
Multimodal Interaction in Intelligent Systems.

/pdf/overview-of-the-2nd-author-profiling-task-at-pan-2014-8filhhi5zp.pdf

Overview of the 2nd Author Profiling Task at PAN 2014

Data and software are immaterial. Scientists in computer science hence have the unique chance to let other scientists easily reproduce their findings. Similarly, and with the same ease, the organization of shared tasks, i.e., the collaborative search for new algorithms given a predefined problem, is possible. Experience shows that the potential of reproducibility is hardly tapped in either case. Based on this observation, and driven by the ambitious goal to find the best solutions for certain problems in our research field, we have been developing the TIRA Integrated Research Architecture. Within TIRA, the reproducibility requirement got top priority right from the start. This chapter introduces the platform, its design requirements, its workflows from both the participants’ and the organizers’ perspectives, alongside a report on user experience and usage scenarios.

TIRA Integrated Research Architecture.

This paper reports on the PAN 2014 evaluation lab which hosts three shared tasks on plagiarism detection, author identification, and author profiling. To improve the reproducibility of shared tasks in general, and PAN’s tasks in particular, the Webis group developed a new web service called TIRA, which facilitates software submissions. Unlike many other labs, PAN asks participants to submit running softwares instead of their run output. To deal with the organizational overhead involved in handling software submissions, the TIRA experimentation platform helps to significantly reduce the workload for both participants and organizers, whereas the submitted softwares are kept in a running state. This year, we addressed the matter of responsibility of successful execution of submitted softwares in order to put participants back in charge of executing their software at our site. In sum, 57 softwares have been submitted to our lab; together with the 58 software submissions of last year, this forms the largest collection of softwares for our three tasks to date, all of which are readily available for further analysis. The report concludes with a brief summary of each task.

/pdf/improving-the-reproducibility-of-pan-s-shared-tasks-3wudcz8wm3.pdf

Improving the Reproducibility of PAN’s Shared Tasks:

With its close ties to the Web, the IR community is destined to leverage the dissemination and collaboration capabilities that the Web provides today. Especially with the advent of the software as a service principle, an IR community is conceivable that publishes experiments executable by anyone over the Web. A review of recent SIGIR papers shows that we are far away from this vision of collaboration. The benefits of publishing IR experiments as a service are striking for the community as a whole, and include potential to boost research profiles and reputation. However, the additional work must be kept to a minimum and sensitive data must be kept private for this paradigm to become an accepted practice. To foster experiments as a service in IR, we present a Web framework for experiments that addresses the outlined challenges and possesses a unique set of compelling features in comparison to existing solutions. We also describe how our reference implementation is already used officially as an evaluation platform for an established international plagiarism detection competition.

Ousting ivory tower research: towards a web framework for providing experiments as a service

Improving the Reproducibility of PAN's Shared Tasks: - Plagiarism Detection, Author Identification, and Author Profiling.

This paper outlines the concepts and achievements of our evaluation lab on digital text forensics, PANi¾?13, which called for original research and development on plagiarism detection, author identification, and author profiling. We present a standardized evaluation framework for each of the three tasks and discuss the evaluation results of the altogether 58i¾?submitted contributions. For the first time, instead of accepting the output of software runs, we collected the softwares themselves and run them on a computer cluster at our site. As evaluation and experimentation platform we use TIRA, which is being developed at the Webis Group in Weimar. TIRA can handle large-scale software submissions by means of virtualization, sandboxed execution, tailored unit testing, and staged submission. In addition to the achieved evaluation results, a major achievement of our lab is that we now have the largest collection of state-of-the-art approaches with regard to the mentioned tasks for further analysis at our disposal.

/pdf/recent-trends-in-digital-text-forensics-and-its-evaluation-25sty9sdut.pdf

Tim Gollub

Papers

TIRA Integrated Research Architecture.

Improving the Reproducibility of PAN’s Shared Tasks:

Ousting ivory tower research: towards a web framework for providing experiments as a service

Improving the Reproducibility of PAN's Shared Tasks: - Plagiarism Detection, Author Identification, and Author Profiling.

Recent Trends in Digital Text Forensics and Its Evaluation