This is lightly edited from an email I sent to colleagues at AI2, so it’s somewhat AI2-specific in places. But, here’s what I thought about EMNLP.

My main takeaway:

Things are moving fast. This was epitomized for me in one of the presentations (I think it was the EpiReader paper, mentioned below), which had a slide about “posterior work” at the end of the talk, discussing four papers that have since surpassed the EpiReader, after the EpiReader paper was posted on arxiv. Similarly, I’m sure most of you are familiar with the SQuAD dataset, as Minjoon Seo built a system for it during his internship at AI2, and his state-of-the-art performance has since been surpassed a couple of times. But did you know that the SQuAD paper was an EMNLP submission? It was just officially presented two days ago, getting the best new resource award. It feels like old news, with system performance already in the 80s (human performance is low 90s). That’s how a lot of the conference felt to me - the action is all happening on arxiv now (and twitter, I guess, where people announce things, but I don’t follow that as much).

Another nice takeaway was that AI2 seems to be getting some good traction in the community - the SQuAD presentation mentioned AI2 as having the state-of-the-art on the dataset for a while, and in several questions at various papers, AI2 was mentioned.

Some area-specific observations:

Dialogue / conversational agents:

All three of the invited talks dealt with conversational agents of various kinds:

Christopher Potts talked about using Gricean principles to build pragmatic, rational speakers and listeners (similar to what Jacob Andreas talked about when he came to visit AI2 a few weeks ago - also an EMNLP paper).

Stefanie Tellex talked about human-robot collaboration, with some cool videos including a robotic forklift that you could talk to, a quadcopter, and a furniture-building collaboration between a person and a robot, where the robot would ask for help using a pragmatic process similar to what Christopher Potts mentioned.

Andreas Stolcke talked about detecting machine-directed speech in human-human-computer interactions. His finding was that we’ve adapted our speech to speak to computers, and so computer-directed speech is pretty easy to spot with acoustic signals alone. I guess we’ll have to find new methods when the computers get better at speech recognition, and we stop adapting our speech so much when we speak to them.

Reading comprehension / memory networks:

Key-value memory networks: a slight modification on the original memory network idea, where you have separate vectors for addressing and reading from memory (except the original memory network did this too, only in a much more hacky way). They also released a large dataset focused on question answering with both a KB and text. The problem is that the questions are generated using templates. Daniel Marcu got up during QA for this talk and basically said that the dataset was garbage, because it was procedurally generated, and Facebook AI Research should stop generating datasets like this (I’m inclined to agree).

EpiReader: a new reading comprehension method, evaluated on CNN/Daily Mail and Facebook’s Children’s Book test datasets, that adds a separate hypothesis-testing step after performing an initial extraction similar to what prior reading comprehension models do.

Who Did What dataset: an improvement on top of the CNN / Daily Mail datasets that uses two news articles for each question, instead of an article and a summary. System performance is quite a bit lower on this new dataset, and human performance is higher. Human performance has basically been reached already on the CNN datasets, but not on this one.

Structured prediction:

There were a ton of papers that used the REINFORCE algorithm to do a simple kind of reinforcement learning for structured prediction models. I need to learn more about how this works…

There was a workshop on structured prediction for NLP with some pretty interesting presentations. Dhruv Batra had a talk about diversity in beam search that I thought was really cool. Basically, you time-delay subsequent particles in the beam, and impose a penalty for later particles using the same predictions as earlier particles. This lets you find several different modes of the posterior distribution, instead of just several particles all from the same mode. He had some nice demos on image segmentation and image captioning presented from http://cloudcv.org/, but I couldn’t find his demos there - they might not be ready for public consumption yet.

Jason Eisner had a tutorial paper at this workshop on the connection between the inside-outside algorithm and backprop, which I thought was a helpful insight.

DyNet tutorial:

I went to a tutorial by Chris Dyer, Yoav Goldberg, and Graham Neubig on DyNet, a neural net library for dynamic computation graphs, instead of the static computation graphs compiled by more common neural net libraries (like theano, tensorflow, caffe, torch, etc.). Structures in language vary across instances much more than they do in images, so dynamic computation graphs are a lot more useful. For example, sentences have varying length, and you might want to gives words their own embedding sometimes, but treat rare words as sequences of characters; this is hard in static computation graphs, but easy in DyNet. You should think of DyNet as a neural net library written by NLP people for NLP problems, whereas most of the other libraries are written by vision people for vision problems. The main drawback of DyNet is that it’s hard to do batching - to do batching you pretty much have to build a static graph anyway, so you lose a lot of the benefits that DyNet gives you. And without batching, DyNet is significantly slower than other frameworks that have static computation graphs.

Another benefit of DyNet is that it should be easier to do structured prediction. Also, Jayant and Oyvind are looking into producing scala bindings for DyNet.

Sentence compression / summarization:

There were a number of papers on sentence compression and abstractive / extractive summarization. This is relevant to Johannes’ internship project, so I went to these talks.

There was a paper by some folks at DeepMind that does sentence compression with discrete latent variables (using language as the compressed representation, instead of some continuous representation). The methodological innovation for this was pretty interesting.

Kristina Toutanova had a paper releasing a dataset for abstractive summarization.

There was a lot of mention of Sasha Rush’s papers as baseline state-of-the-art models for this work.

Kristina told me that Greg Durrett had some code and models available that would be a good thing to try for Johannes’ project.

Other interesting papers:

Chloe Kiddon (a recently-graduated UW student) had an interesting paper on generating recipes given an ingredient list and a recipe title. The interesting innovation here was keeping track of which ingredients had been used already in a soft way, with neural techniques. This reminded me of the used/free vector for memory cells in the differential neural computer by DeepMind. I wonder if you could get this kind of general framework to perform similarly to the specific model that Chloe used.

Some papers and notes that are relevant for Semantic Scholar: