Scroll down for a detailed description of this project's motivation and structure.
If you are interested in this project, please contact the instructor -- Shane Steinert-Threlkeld -- by e-mail: S.N.M.Steinert-Threlkeld (at) uva.nl.
In this project, students will develop tools and run (computational) experiments to test the hypothesis that semantic universals arise because expressions satisfying them are easier to learn than those that do not.
A semantic universal is a property of meaning shared by (almost) all natural languages (possibly conditional on the languages having additional properties). Because languages vary quite a bit, when one finds a universal, one naturally wonders whether there's an explanation for it. Why do all languages have this semantic property? We are interested in exploring the following hypothesis.
Hypothesis: Semantic universals arise because they make meaning systems easier to learn.
Of course, the hypothesis can only be supported or refuted when a model of learning a semantic system has been specified. Thus, the hypothesis naturally gives rise to a challenge.
Challenge: Provide a model of learning which makes good on the Hypothesis (at least, for some semantic universals).
In recent work, Steinert-Threlkeld and Szymanik attempt to meet the Challenge by training recurrent neural networks to learn the meanings of quantifiers, a domain where many semantic universals have been posited. They use this framework to explain universals like monotonicity and quantity. The monotonicity universal works as follows. Consider the following two sentences.
Sentence (1) entails sentence (2): the former cannot be true without the latter being true. Notice that all we have done is replaced the term "smoke cigarettes" with the strictly more general term "smoke". Also notice that the inference pattern holds for any choice of the restrictor (not just "French people") and pairs of nuclear scope that stand in the same specific-general relation. Because of this, we say that the quantifier "many" is upward monotone. If "many" is replaced by "few", the inference pattern reverses. "few" is downward monotone. The proposed universal then states:
Monotonicity: All simple determiners are monotone.
In the paper, neural networks are trained to learn monotone and non-monotone quantifiers and it is shown that the former are learned significantly faster than the latter. We also show that this pattern holds for another universal called Quantity, which states that quantifiers only care about the sizes of sets, and not the identity of objects or their position in a structure. (This is related to a conception of logicality.)
In this project, students will develop new tools and run more experiments in order to further develop the explanation of semantic universals in terms of learnability. They will be doing original research that could become (part of) publications. Existing code as well as access to computing infrastructure will be provided. Possible topics that can be addressed are:
The class will meet 3 times a week in the four weeks from January 8 to February 2. We will provide necessary background in the first two weeks, then transition into coding / experimenting sessions in the last two weeks. The course should be mostly self-contained, though some pre-requisites are listed.
Week 1: theoretical background on semantic universals and quantifiers (possibly color as well)
Week 2: background on training neural networks, tutorials on how to run your own experiments by modifying provided code
Week 3: run experiments! We will have in-class coding sessions for support and question answering.
Week 4: finish experiments; write up the results and deliver short presentation
Working knowledge of Python will be very valuable. Knowledge of specific libraries (Numpy, TensorFlow/Keras) will be helpful, but can be learned on the fly. While we will cover neural networks and their training and evaluation in the second week, familiarity with those topics will also help.
Students will be conducting their own (computational) experiments. They will be expected to produce a short write-up (~5 pages) of at least one experimental result, explaining the motivation for their experiment and what they found. On the final day of class, there will be short presentations of results and discussion.
Here we include both reading and coding resources.