We show that by changing the grammar of the formal meaning representation language and training on additional data collected from Amazon’s Mechanical Turk we can further improve the results.

We validate this claim by collecting additional training data for the navigation domain using Mechanical Turk (Snow et al., 2008).

Thus, we created two tasks on Mechanical Turk .

The instructions used for the follower problems were mainly collected from our Mechanical Turk instructor task with some of the instructions coming from data collected by MacMahon (2007) that was not used by Chen and Mooney (2011).

In addition, we showed that changing the MRG and collecting additional training data from Mechanical Turk further improve the performance of the overall navigation system.

In addition to SGOLL and SGOLL with the new MRG, we also look at augmenting each of the training splits with the data we collected using Mechanical Turk .

training data with additional instructions and follower traces collected from Mechanical Turk produced the best results.

Even after incorporating the new Mechanical Turk data into the training set, SGOLL still takes much less time to build a lexicon.

gorithm can scale to larger datasets, we present results on collecting and training on additional data from Amazon’s Mechanical Turk .

In this paper we have described a dynamic and inexpensive method of collecting a corpus of questions and answers using the Amazon Mechanical Turk framework.

The total cost of producing the corpus was about $350, consisting of $310 paid in workers rewards and $40 in Mechanical Turk fees, including all the trials conducted during the development of the final system.

However, the Mechanical Turk framework provided additional information for each assignment, for example the time workers spent on the task.

We believe that this unexpected result is due to the distributed nature of the worker pool in Mechanical Turk .

We collected our corpus through Amazon Mechanical Turk service 4 (MTurk).

2.1 Mechanical Turk

Mechanical Turk is a Web-based service, offered by Amazon.com, Inc.

To evaluate the methods, we collected examples of question—answer pairs involving scalar modifiers from CNN transcripts and the Dialog Act corpus and use response distributions from Mechanical Turk workers to assess the degree to which each answer conveys ‘yes’ or ‘no’.

Table 2: Mean entropy values and standard deviation obtained in the Mechanical Turk experiment for each question—answer pair category.

To assess the degree to which each answer conveys ‘yes’ or ‘no’ in context, we use response distributions from Mechanical Turk workers.

Despite variant individual judgments, aggregate annotations done with Mechanical Turk have been shown to be reliable (Snow et a1., 2008; Sheng et a1., 2008; Munro et a1., 2010).

To evaluate the techniques, we pool the Mechanical Turk ‘definite yes’ and ‘probable yes’ categories into a single category ‘Yes’, and we do the same for ‘definite no’ and ‘probable no’.

Due to the relatively high speed and low cost of Amazon’s Mechanical Turk serVice, we chose to use Mechanical Turkers as our annotators.

Using Mechanical Turk to obtain inter-annotator agreement figures has several drawbacks.

We then embarked on a series of changes, testing each generation by annotation using Amazon’s Mechanical Turk service, a relatively quick and inexpensive online platform where requesters may publish tasks for anonymous online workers (Turkers) to perform.

Mechanical Turk has been previously used in a variety of NLP research, including recent work on noun compounds by Nakov (2008) to collect short phrases for linking the nouns within noun compounds.

For the Mechanical Turk annotation tests, we created five sets of 100 noun compounds from noun compounds automatically extracted from a random subset of New York Times articles written between 1987 and 2007 (Sandhaus, 2008).

Deploying the framework on Mechanical Turk over a two-month period yielded 85K English descriptions for 2K videos, one of the largest paraphrase data resources publicly available.

We deployed the task on Amazon’s Mechanical Turk , with video segments selected from YouTube.

Figure l: A screenshot of our annotation task as it was deployed on Mechanical Turk .

We deployed our data collection framework on Mechanical Turk over a two-month period from July to September in 2010, collecting 2,089 video segments and 85,550 English descriptions.

We randomly selected a thousand sentences from our data and collected two paraphrases of each using Mechanical Turk .

We designed our data collection framework for use on crowdsourcing platforms such as Amazon’s Mechanical Turk .

3.1 Mechanical Turk Study

We deployed an annotation study on Amazon Mechanical Turk (MTurk)3, a crowdsourcing platform for tasks requiring human input.

For training and testing data, we used the labeled data from the Mechanical Turk study.

A Mechanical Turk study we carried out revealed that there is a significant correlation between objectivity of language and trustworthiness of sources.

To test this hypothesis, we designed a Mechanical Turk study.

(3) Objectivity Classifier: Using labeled data from the Mechanical Turk study, we developed and trained an objectivity classifier which performed better than prior proposed lexicons from literature.

In this paper we study a large, low quality annotated dataset, created quickly and cheaply using Amazon Mechanical Turk to crowd-source annotations.

We then sent these tweets to Amazon Mechanical Turk for annotation.

In order to evaluate our approach in real world scenarios, instead of creating a high quality annotated dataset and then introducing artificial noise, we followed the common practice of crowdsouc-ing, and collected emotion annotations through Amazon Mechanical Turk (AMT).

Amazon Mechanical Turk Annotation: we posted the set of 100K tweets to the workers on AMT for emotion annotation.

There are generally two ways to collect annotations of a dataset: through a few expert annotators, or through crowdsourcing services (e.g., Amazon’s Mechanical Turk ).

We employ Amazon’s Mechanical Turk (AMT) to label the emotions of Twitter data, and apply the proposed methods to the AMT dataset with the goals of improving the annotation quality at low cost, as well as learning accurate emotion classifiers.

This data set consists 1,792 Urdu sentences from a variety of news and online sources, each paired with English translations provided by nonprofessional translators on Mechanical Turk .

Rather than relying on volunteers or gamifica-tion, NLP research into crowdsourcing translation has focused on hiring workers on the Amazon Mechanical Turk (MTurk) platform (Callison-Burch, 2009).

Our setup uses anonymous crowd workers hired on Mechanical Turk , whose motivation to participate is financial.

Most NLP research into crowdsourcing has focused on Mechanical Turk , following pioneering work by Snow et al.

Although hiring professional translators to create bilingual training data for machine translation systems has been deemed infeasible, Mechanical Turk has provided a low cost way of creating large volumes of translations (Callison-Burch, 2009; Ambati and Vogel, 2010).

We hope to engage these algorithms with more sophisticated users than those on Mechanical Turk to measure how these models can help them better explore and understand large, uncurated data sets.

We solicited approximately 200 judgments from Mechanical Turk , a popular crowdsourcing platform that has been used to gather linguistic annotations (Snow et a1., 2008), measure topic quality (Chang et al., 2009), and supplement traditional inference techniques for topic models (Chang, 2010).

Figure 5 shows the interface used in the Mechanical Turk tests.

Figure 5: Interface for Mechanical Turk experiments.

We tested the suggestions generated by our algorithm via a Mechanical Turk experiment, which showed a significant improvement over the strongest baseline of more than 45% in all metrics.

We assessed the performance of our algorithm via a Mechanical Turk experiment.

To evaluate our algorithm’s performance, we designed a Mechanical Turk (MTurk) experiment in which human annotators assess the quality of the questions that our algorithm generates for a sample of news articles.

To test the performance of our algorithm, we conducted a Mechanical Turk experiment that assessed the quality of suggested questions for news articles on celebrities.

We used Amazon Mechanical Turk (AMT) to collect ratings of claims.

This dataset was annotated by Mechanical Turk workers who gave ratings for the factuality of the scoped claims in each Twitter message.

While these findings must be interpreted with caution, they suggest that readers — at least, Mechanical Turk workers — use relatively little independent judgment to assess the validity of quoted text that they encounter on Twitter.

(2012) conduct an empirical evaluation of FactBank ratings from Mechanical Turk workers, finding a high degree of disagreement between raters.

(2008) includes 10 non-expert annotations for each of the 800 items in the RTEl testset, collected with Amazon’s Mechanical Turk .

In recent years, the possibility to undertake large-scale annotation projects with hundreds or thousands of annotators has become a reality thanks to online crowdsourcing methods such as Amazon’s Mechanical Turk and Games with a Purpose.

Similarly, crowdsourcing via microworking sites like Amazon’s Mechanical Turk has been used in several annotation experiments related to tasks such as affect analysis, event annotation, sense definition and word sense disambiguation (Snow et al., 2008; Rumshisky, 2011; Rumshisky et al., 2012), amongst others.12

12See also the papers presented at the NAACL 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk (t inyurl .

Using an artificial language vocabulary, we evaluate a set of algorithms for generating code-switched text automatically by presenting it to Mechanical Turk subjects and measuring recall in a sentence completion task.

We carried out experiments on the effectiveness of our approach using the Amazon Mechanical Turk platform.

For collecting data about which words are likely to be “predicted” given their content, we developed an Amazon Mechanical Turk task that presented turkers with excerpts of a short story (English translation of “The Man who Repented” by

In an Amazon Mechanical Turk evaluation, users pref-ered SUMMA ten times as often as flat MDS and three times as often as timelines.

We hired Amazon Mechanical Turk (AMT) workers and assigned two topics to each worker.

We conducted an Amazon Mechanical Turk (AMT) evaluation where AMT workers compared the output of SUMMA to that of timelines and flat summaries.

We want to thank Dustin Smith for the OMICS data, Alexis Palmer for her support with Amazon Mechanical Turk , Nils Bendfeldt for the creation of all web forms and Ines Rehbein for her effort

We presented each pair to 5 non-experts, all US residents, via Mechanical Turk .

In particular, the use of the Amazon Mechanical Turk , which we use here, has been evaluated and shown to be useful for language processing tasks (Snow et al., 2008).

In an Amazon Mechanical Turk (AMT) experiment (§4), we found that humans achieved an average accuracy of 61.3%: not that high, but better than chance, indicating that it is somewhat possible for humans to predict greater message spread from different deliveries of the same information.

We first ran a pilot study on Amazon Mechanical Turk (AMT) to determine whether humans can identify, based on wording differences alone, which of two topic- and author- controlled tweets is spread more widely.

We outperform the average human accuracy of 61% reported in our Amazon Mechanical Turk experiments (for a different data sample); fiTAC+ff+time fails to do so.

Annotation errors can significantly hurt classifier performance, yet datasets are only growing noisier with the increased use of Amazon Mechanical Turk and techniques like distant supervision that automatically generate labels.

The data was created by taking various Wikipedia articles and giving them to five Amazon Mechanical Turkers to annotate.

Low-quality annotations have become even more common in recent years with the rise of Amazon Mechanical Turk , as well as methods like distant supervision and co-training that involve automatically generating training data.

Human evaluation was performed by evaluators on Amazon’s Mechanical Turk service, shown to be effective for natural language annotation in Snow et al.

For each of the 10 relations that appeared most frequently in our test data (according to our classifier), we took samples from the first 100 and 1000 instances of this relation generated in each experiment, and sent these to Mechanical Turk for

Each predicted relation instance was labeled as true or false by between 1 and 3 labelers on Mechanical Turk .