They created a really nice trick to optimize the $ {L}_{0} $ Pseudo Norm - Regularization on the sorted (By magnitude) values of the optimization variable.

Their code is available at - [The Trimmed Lasso: Sparsity and Robustness](https://github.com/copenhaver/trimmedlasso).

The paper explores the usefulness of eye tracking for the task of POS tagging. The assumption is that readers skip quickly over closed class words, and fixate longer on rare on ambiguous words.

The experiments are performed on unsupervised POS tagging – a second-order HMM uses constraints on possible tags for each word (based on a dictionary), but no explicit annotated data is required. They show that including the eye tracking features improves performance by quite a bit. Surprisingly, it seems to be better to average eye tracking features over all training tokens of the same type, as opposed to using using the data for each individual token, which means eye tracking is only used during the training stage.

This paper proposes a new training method for multi-agent communication settings. They show the following referential game: A speaker sees an image of a 3d rendered object and describes it to a listener. The listener sees a different image and must decide if it is the same object as described by the speaker (has the same color and shape). The game can only be completed successfully if a communication protocol emerges that can express the color and shape the speaker sees.

The main contribution of the paper is the training algorithm. The speaker enumerates the message that would maximise its own understanding of the message given the image it sees (in a greedy way, symbol by symbol). The listener, given the image and the message, predicts a binary output and is trained using maximum likelihood given the correct answer. Only the listener is updating its parameters - therefore the speaker and listener change roles every number of rounds.

They show that a compositional communication protocol has emerged and evaluate it using zero-shot tests.

[Implemenation of this paper in pytorch](https://github.com/benbogin/obverter)

The authors investigate the benefit of different task combinations when performing multi-task learning.

https://i.imgur.com/VmD2ioS.png

They experiment with all possible pairs of 10 sequence labeling datasets, switching between the datasets during training. They find that multi-task learning helps more when the main task quickly plateaus while the auxiliary task does not, likely helping the model out of local minima.
There does not seem to be any auxiliary task that would help on all main tasks, but chunking and semantic tagging seem to perform best.

This paper is, on the whole, a refreshing jaunt into the applied side of the research word. It isn’t looking to solve a fundamental machine learning problem in some new way, but it does highlight and explore one potential beneficial application of a common and widely used technique: specifically, combining word embeddings with context-free grammars (such as: regular expressions), to make the latter less rigid. 

Regular expressions work by specifying specific hardcoded patterns of symbols, and matching against any strings in some search set that match those patterns. They don’t need to specify specific characters - they can work at higher levels of generality, like “uppercase alphabetic character” or “any character”, but they’re still fundamentally hardcoded, in that the designer of the expression needs to create a specification that will affirmatively catch all the desired cases. This can be a particular challenging task when you’re trying to find - for example - all sentences that match the pattern of someone giving someone else a compliment. You might want to match against “I think you’re smart” and also “I think you’re clever”. However, in the normal use of regular expressions, something like this would be nearly impossible to specify, short of writing out every synonym for “intelligent” that you can think of. 

The “Embedding Grammars” paper proposes a solution to this problem: instead of enumerating a list of synonyms, simply provide one example term, or, even better, a few examples, and use those term’s word embedding representation to define a “synonym bubble” (my word, not theirs) in continuous space around those examples. This is based on the oft-remarked-upon fact that, because word embedding systems are generally trained to push together words that can be used in similar contexts, closeness in word vector space frequently corresponds to words being synonyms, or close in some other sense. So, if you “match” to any term that is sufficiently nearby to your exemplar terms, you are performing something similar to the task of enumerating all of a term’s syllables. Once this general intuition is in hand, the details of the approach are fairly straightforward: the authors try a few approaches, and find that constructing a bubble of some epsilon around each example’s word vector, and matching to anything inside that bubble, works the best as an approach. 

https://i.imgur.com/j9OSNuE.png

Overall, this seems like a clever idea; one imagines that the notion of word embeddings will keep branching out into ever more far-flung application as time goes on. There are reasons to be skeptical of this paper, though. Fundamentally, word embedding space is a “here there be dragons” kind of place: we may be able to observe broad patterns, and might be able to say that “nearby words tend to be synonyms,” but we can’t give any kind of guarantee of that being the case. As an example of this problem, often the nearest thing to an example, after direct synonyms, are direct antonyms, so if you set too high a threshold, you’ll potentially match to words exactly the opposite of what you expect. We are probably still a ways away from systems like this one being broady useful, for this and other reasons, but I do think it’s valuable to try to understand what questions we’d want answered, what features of embedding space we’d want more elucidated, before applications like these would be more stably usable.