My PhD research had me dig into Amazon Mechanical Turk more than I wished to. Together with CrowdFlower/Figure8, those are the default names that come up whenever one thinks of annotation but, after much research, I realized that the right tool is much simpler.
Crowd-sourcing tools are for, ahem, crowds — a mass of unknown people of unknown characteristics. But not every task can be done by a crowd. Continue reading
In the newly developed Oxford-Munich code of conduct for data scientists (code-of-ethics.org), one can find the following articles that obligate data scientists to ensure replicability:
3b(iv). Artificial data handling
The Data Scientist is responsible for communicating all the procedures employed to make the original data more adequate for the specific problem, especially techniques intended to correct gaps in the data, to balance classification problems, e.g. Interpolation, extrapolation, oversampling and under-sampling. As far as possible, these procedures should be peer-reviewed.
4l. AI Reproducibility
Most of the models created by data scientists have stochastic components, meaning there is no guarantee that the same model will be produced given the same training data. Moreover, it’s a known issue, that fixing a seed to force reproducibility compromises the parallelization of the models.
The Data Scientist shall be responsible to ensure reproducibility in situations where understanding the overall behavior of the system is critical.
Hopefully, this initiative will make industrial and academic researchers more aware of replicability issues and improve their procedures.
At EACL last year, I had a lengthy chat with a guy next to his poster about the (ir)replicability of some high-profile papers in information retrieval . During some 5 years of research that I’ve gone through, I also often ran into reproducibility problems. Probably many PhD students out there have relatable experiences.
Obviously, researchers should take full responsibility to produce replicable research. But we should also recognize the underlying systemic issue. Researchers are not rewarded to make their work repeatable. Once a paper is accepted, you are already in the middle of a new one so there’s no time to make your old code re-runable (if that’s possible at all). Added to that, the likelihood (or threat) of your work being reproduced is terribly small. There are not many reports of reproducibility problem in NLP and retracted papers are non-existent. While big conferences are starting to address this problem (COLING 2018 has a track for reproduction and LREC 2018 also mentions “replicability and reproducibility issues”), I suspect it will take years for the effect to be felt.
In the meantime, what we could do is to align the effort to the incentive. Ideally, it should take no extra work to make your research replicable. The solution, I think, is to make experiments replicable by design. Continue reading
It’s been one month and a half that I found myself working at Elsevier. Five years ago when I was in Italy working on a Vietnamese news recommendation service, it didn’t come to my mind that the work would earn me a good job in yet another country. I’m grateful for what life brings my way. However, five years is a long stretch of time for human memory and the state-of-the-art surely has changed a lot. For one, these shiny toys called deep neural networks keep popping up everywhere. So a local event on the field seems good to attend.
RecSys Amsterdam meetup is an informal meeting of recommender researchers and practitioners in Amsterdam. It used to be held once a year but because of a surge in interest, now every four months or so. The 9th installment that I attended is a small gathering (there’re 140 attendees on the web but it felt more like 60) with companies (or academics that interned at a company) showcasing their cool stuff and, of course, approaching potential hires. I think we had a good mix of Microsoft, FD Mediagroep, and Booking.com. Continue reading
Batchkarov et al. (2016) is one of evaluation/methodology papers much needed in NLP and I hope we’ll have more of them. But I think w.r.t. statistical methodology, the paper is troublesome or at least not good enough for ACL. In this short report, I explain why.
Critical evaluation of word similarity datasets is very important for computational lexical semantics. This short report concerns the sanity check proposed in Batchkarov et al. (2016) to evaluate several popular datasets such as MC, RG and MEN — the first two reportedly failed. I argue that this test is unstable, offers no added insight, and needs major revision in order to fulfill its purported goal.
I got 52% play Phrase Detective on Facebook. How could I get a PhD in Natural Language Processing?
Just kidding, I’m not worrying at all about graduation but just a bit surprised by some features of the game. I’m studying the possibility of running a crowd-sourcing task on coreference resolution so I’m very much interested in how to do crowd-sourcing properly. So these are the things that I found surprising: Continue reading
Statistical machine learning has been the de-facto standard in NLP research and practice. However, its very success might be hiding its the problems. One such problem is exceptions.
Natural language is full of exceptions: idiomatic phrases that defy compositionality, irregular verbs and exceptions to grammatical rules, or unexpected events that, though not linguistic phenomena themselves, happen to be communicated via language. So far, statistical NLP has treated them as inconvenient oddity and, in most cases, swept them under the rug, hoping that they wouldn’t reduce F-score.
But a system doesn’t really understand language without handling exceptions and I will argue that (not) handling exceptions has important consequences to machine learning. Continue reading
A quick note from EACL: some papers related to LSDSem workshop (Bugert et al. 2017; Zhou et al. 2015) use McNemar’s test to establish statistical significance and I find it very odd.
McNemar’s test examine “marginal (probability) homogeneity” which in our case is whether two systems yield (statistically) the same performance. According to the source code I found on Github, the way it works is:
- Obtain predictions of System 1 and System 2
- Compare them to gold labels to fill this table:
- Compute the test statistics: and p-value
- If p-value is less than a certain level (e.g. the magical 0.05), we reject the null hypothesis which is p(Sys1 correct) == p(Sys2 correct)
As it happens in the papers, the difference is statistically significant and therefore results are meaningful. Happy?
Not so fast. Continue reading
I was reading Clark and Manning (2016) and studying their code. The contrast is just amazing.
This is what the paper has to say:
This is what I found after 1 hour of reading a JSON file and writing down all layers of the neural net (the file is
data/models/all_pairs/architecture.json, created when you run the experiment):
Without the source code, this would be a replication nightmare for sure.
Clark, K., & Manning, C. D. (2016). Improving Coreference Resolution by Learning Entity-Level Distributed Representations. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 643–653. http://doi.org/10.18653/v1/P16-1061
Title: Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing
Conference: EACL 2017 (European Chapter of the Association for Computational Linguistics), at Valencia, 3-7 April 2017.
Error propagation is a common problem in NLP. Reinforcement learning explores erroneous states during training and can therefore be more robust when mistakes are made early in a process. In this paper, we apply reinforcement learning to greedy dependency parsing which is known to suffer from error propagation. Reinforcement learning improves accuracy of both labeled and unlabeled dependencies of the Stanford Neural Dependency Parser, a high performance greedy parser, while maintaining its efficiency. We investigate the portion of errors which are the result of error propagation and confirm that reinforcement learning reduces the occurrence of error propagation.
Full article: arXiv:1702.06794
Slides: view online