Jon Chamberlain


2021

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SemEval-2021 Task 12: Learning with Disagreements
Alexandra Uma | Tommaso Fornaciari | Anca Dumitrache | Tristan Miller | Jon Chamberlain | Barbara Plank | Edwin Simpson | Massimo Poesio
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision. However, most supervised machine learning methods assume that a single preferred interpretation exists for each item, which is at best an idealization. The aim of the SemEval-2021 shared task on learning with disagreements (Le-Wi-Di) was to provide a unified testing framework for methods for learning from data containing multiple and possibly contradictory annotations covering the best-known datasets containing information about disagreements for interpreting language and classifying images. In this paper we describe the shared task and its results.

2020

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Cipher: A Prototype Game-with-a-Purpose for Detecting Errors in Text
Liang Xu | Jon Chamberlain
Workshop on Games and Natural Language Processing

Errors commonly exist in machine-generated documents and publication materials; however, some correction algorithms do not perform well for complex errors and it is costly to employ humans to do the task. To solve the problem, a prototype computer game called Cipher was developed that encourages people to identify errors in text. Gamification is achieved by introducing the idea of steganography as the entertaining game element. People play the game for entertainment while they make valuable annotations to locate text errors. The prototype was tested by 35 players in a evaluation experiment, creating 4,764 annotations. After filtering the data, the system detected manually introduced text errors and also genuine errors in the texts that were not noticed when they were introduced into the game.

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Aggregation Driven Progression System for GWAPs
Osman Doruk Kicikoglu | Richard Bartle | Jon Chamberlain | Silviu Paun | Massimo Poesio
Workshop on Games and Natural Language Processing

As the uses of Games-With-A-Purpose (GWAPs) broadens, the systems that incorporate its usages have expanded in complexity. The types of annotations required within the NLP paradigm set such an example, where tasks can involve varying complexity of annotations. Assigning more complex tasks to more skilled players through a progression mechanism can achieve higher accuracy in the collected data while acting as a motivating factor that rewards the more skilled players. In this paper, we present the progression technique implemented in Wormingo , an NLP GWAP that currently includes two layers of task complexity. For the experiment, we have implemented four different progression scenarios on 192 players and compared the accuracy and engagement achieved with each scenario.

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Speaking Outside the Box: Exploring the Benefits of Unconstrained Input in Crowdsourcing and Citizen Science Platforms
Jon Chamberlain | Udo Kruschwitz | Massimo Poesio
Proceedings of the LREC 2020 Workshop on "Citizen Linguistics in Language Resource Development"

Crowdsourcing approaches provide a difficult design challenge for developers. There is a trade-off between the efficiency of the task to be done and the reward given to the user for participating, whether it be altruism, social enhancement, entertainment or money. This paper explores how crowdsourcing and citizen science systems collect data and complete tasks, illustrated by a case study from the online language game-with-a-purpose Phrase Detectives. The game was originally developed to be a constrained interface to prevent player collusion, but subsequently benefited from posthoc analysis of over 76k unconstrained inputs from users. Understanding the interface design and task deconstruction are critical for enabling users to participate in such systems and the paper concludes with a discussion of the idea that social networks can be viewed as form of citizen science platform with both constrained and unconstrained inputs making for a highly complex dataset.

2019

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Crowdsourcing and Aggregating Nested Markable Annotations
Chris Madge | Juntao Yu | Jon Chamberlain | Udo Kruschwitz | Silviu Paun | Massimo Poesio
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

One of the key steps in language resource creation is the identification of the text segments to be annotated, or markables, which depending on the task may vary from nominal chunks for named entity resolution to (potentially nested) noun phrases in coreference resolution (or mentions) to larger text segments in text segmentation. Markable identification is typically carried out semi-automatically, by running a markable identifier and correcting its output by hand–which is increasingly done via annotators recruited through crowdsourcing and aggregating their responses. In this paper, we present a method for identifying markables for coreference annotation that combines high-performance automatic markable detectors with checking with a Game-With-A-Purpose (GWAP) and aggregation using a Bayesian annotation model. The method was evaluated both on news data and data from a variety of other genres and results in an improvement on F1 of mention boundaries of over seven percentage points when compared with a state-of-the-art, domain-independent automatic mention detector, and almost three points over an in-domain mention detector. One of the key contributions of our proposal is its applicability to the case in which markables are nested, as is the case with coreference markables; but the GWAP and several of the proposed markable detectors are task and language-independent and are thus applicable to a variety of other annotation scenarios.

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A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation
Massimo Poesio | Jon Chamberlain | Silviu Paun | Juntao Yu | Alexandra Uma | Udo Kruschwitz
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We present a corpus of anaphoric information (coreference) crowdsourced through a game-with-a-purpose. The corpus, containing annotations for about 108,000 markables, is one of the largest corpora for coreference for English, and one of the largest crowdsourced NLP corpora, but its main feature is the large number of judgments per markable: 20 on average, and over 2.2M in total. This characteristic makes the corpus a unique resource for the study of disagreements on anaphoric interpretation. A second distinctive feature is its rich annotation scheme, covering singletons, expletives, and split-antecedent plurals. Finally, the corpus also comes with labels inferred using a recently proposed probabilistic model of annotation for coreference. The labels are of high quality and make it possible to successfully train a state of the art coreference resolver, including training on singletons and non-referring expressions. The annotation model can also result in more than one label, or no label, being proposed for a markable, thus serving as a baseline method for automatically identifying ambiguous markables. A preliminary analysis of the results is presented.

2018

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A Probabilistic Annotation Model for Crowdsourcing Coreference
Silviu Paun | Jon Chamberlain | Udo Kruschwitz | Juntao Yu | Massimo Poesio
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

The availability of large scale annotated corpora for coreference is essential to the development of the field. However, creating resources at the required scale via expert annotation would be too expensive. Crowdsourcing has been proposed as an alternative; but this approach has not been widely used for coreference. This paper addresses one crucial hurdle on the way to make this possible, by introducing a new model of annotation for aggregating crowdsourced anaphoric annotations. The model is evaluated along three dimensions: the accuracy of the inferred mention pairs, the quality of the post-hoc constructed silver chains, and the viability of using the silver chains as an alternative to the expert-annotated chains in training a state of the art coreference system. The results suggest that our model can extract from crowdsourced annotations coreference chains of comparable quality to those obtained with expert annotation.

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Scalable Visualisation of Sentiment and Stance
Jon Chamberlain | Udo Kruschwitz | Orland Hoeber
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Comparing Bayesian Models of Annotation
Silviu Paun | Bob Carpenter | Jon Chamberlain | Dirk Hovy | Udo Kruschwitz | Massimo Poesio
Transactions of the Association for Computational Linguistics, Volume 6

The analysis of crowdsourced annotations in natural language processing is concerned with identifying (1) gold standard labels, (2) annotator accuracies and biases, and (3) item difficulties and error patterns. Traditionally, majority voting was used for 1, and coefficients of agreement for 2 and 3. Lately, model-based analysis of corpus annotations have proven better at all three tasks. But there has been relatively little work comparing them on the same datasets. This paper aims to fill this gap by analyzing six models of annotation, covering different approaches to annotator ability, item difficulty, and parameter pooling (tying) across annotators and items. We evaluate these models along four aspects: comparison to gold labels, predictive accuracy for new annotations, annotator characterization, and item difficulty, using four datasets with varying degrees of noise in the form of random (spammy) annotators. We conclude with guidelines for model selection, application, and implementation.

2016

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Phrase Detectives Corpus 1.0 Crowdsourced Anaphoric Coreference.
Jon Chamberlain | Massimo Poesio | Udo Kruschwitz
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Natural Language Engineering tasks require large and complex annotated datasets to build more advanced models of language. Corpora are typically annotated by several experts to create a gold standard; however, there are now compelling reasons to use a non-expert crowd to annotate text, driven by cost, speed and scalability. Phrase Detectives Corpus 1.0 is an anaphorically-annotated corpus of encyclopedic and narrative text that contains a gold standard created by multiple experts, as well as a set of annotations created by a large non-expert crowd. Analysis shows very good inter-expert agreement (kappa=.88-.93) but a more variable baseline crowd agreement (kappa=.52-.96). Encyclopedic texts show less agreement (and by implication are harder to annotate) than narrative texts. The release of this corpus is intended to encourage research into the use of crowds for text annotation and the development of more advanced, probabilistic language models, in particular for anaphoric coreference.

2009

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Constructing an Anaphorically Annotated Corpus with Non-Experts: Assessing the Quality of Collaborative Annotations
Jon Chamberlain | Udo Kruschwitz | Massimo Poesio
Proceedings of the 2009 Workshop on The People’s Web Meets NLP: Collaboratively Constructed Semantic Resources (People’s Web)

2008

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ANAWIKI: Creating Anaphorically Annotated Resources through Web Cooperation
Massimo Poesio | Udo Kruschwitz | Jon Chamberlain
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

The ability to make progress in Computational Linguistics depends on the availability of large annotated corpora, but creating such corpora by hand annotation is very expensive and time consuming; in practice, it is unfeasible to think of annotating more than one million words. However, the success of Wikipedia and other projects shows that another approach might be possible: take advantage of the willingness of Web users to contribute to collaborative resource creation. AnaWiki is a recently started project that will develop tools to allow and encourage large numbers of volunteers over the Web to collaborate in the creation of semantically annotated corpora (in the first instance, of a corpus annotated with information about anaphora).

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Addressing the Resource Bottleneck to Create Large-Scale Annotated Texts
Jon Chamberlain | Massimo Poesio | Udo Kruschwitz
Semantics in Text Processing. STEP 2008 Conference Proceedings