Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and even execute symbolic instructions as first-person actors in partially-observable worlds. To achieve this so-called grounded language learning, models must overcome certain well-studied learning challenges that are also fundamental to infants learning their first words. While it is notable that models with no meaningful prior knowledge overcome these learning obstacles, AI researchers and practitioners currently lack a clear understanding of exactly how they do so. Here we address this question as a way of achieving a clearer general understanding of grounded language learning, both to inform future research and to improve confidence in model predictions.
For maximum control and generality, we focus on a simple neural network-based language learning agent trained via policy-gradient methods to interpret synthetic linguistic instructions in a simulated 3D world. We apply experimental paradigms from developmental psychology to this agent, exploring the conditions under which established human biases and learning effects emerge. We further propose a novel way to visualise and analyse semantic representation in grounded language learning agents that yields a plausible algorithmic account of the observed effects.
This paper presents an analysis of an agent trained to follow linguistic commands in a 3D environment.  The behaviour of the agent is analyzed by means of a set of "psycholinguistic" experiments probing what it learned, and by inspection of its visual component through an attentional mechanism.
On the positive side, it is nice to read a paper that focuses on understanding what an agent is learning. On the negative side, I did not get many new insights from the analyses presented in the study.
3 A situated language learning agent
I can't make up the chair from the refrigerator in the figure.
4.1 Word learning biases
This experiment shows that, when an agent is trained on shapes only, it will exhibit a shape bias when tested on new shapes and colors. Conversely, when it is exposed to colors only, it will have a color bias. When the training set is balanced, the agent shows a mild bias for the simpler color property. How is this interesting or surprising? The crucial question, here, would be whether, when an agent is trained in a naturalistic environment (i.e., where distributions of colors, shapes and other properties reflect those encountered by biological agents), it would show a human-like shape bias. This, however, is not addressed in the paper.
Minor comments about this section:
- Was there noise also in shape generation, or were all object instances identical?
- propensity to select o_2: rather o_1?
- I did not follow the paragraph starting with "This effect provides".
4.2 The problem of learning negation
I found this experiment very interesting.
Perhaps, the authors could be more explicit about the usage of negation here. The meaning of commands containing negation are, I think, conjunctions of the form "pick something and do not pick X" (as opposed to the more natural "do not pick X").
modifiation: modification
4.3 Curriculum learning
Perhaps the difference in curriculum effectiveness in language modeling vs grounded language learning simulations is due to the fact that the former operates on large amounts of natural data, where it's hard to define the curriculum, while the latter are typically grounded in toy worlds with a controlled language, where it's easier to construct the curriculum.
4.4 Processing and representation differences
There is virtually no discussion of what makes the naturalistic setup naturalistic, and thus it's not clear which conclusions we should derive from the corresponding experiments. Also, I don't see what we should learn from Figure 5 (besides the fact that in the controlled condition shapes are easier than categories). For the naturalistic condition, the current figure is misleading, since different classes contain different numbers of instances. It would be better to report proportions.
Concerning the attention analysis, it seems to me that all it's saying is that lower layers of a CNN detect lower-level properties such as colors, higher layers detect more complex properties, such as shapes characterizing objects. What is novel here?
Also, since introducing attention changes the architecture, shouldn't the paper report the learning behaviour of the attention-augmented network?
The explanation of the attention mechanism is dense, and perhaps could be aided by a diagram (in the supplementary materials?). I think the description uses "length" when "dimensional(ity)" is meant.
6. Supplementary material
It would be good to have an explicit description of the architecture, including number of layers of the various components, structure of the CNN, non-linearities, dimensionality of the layers, etc. (some of this information is inconsistently provided in the paper).
It's interesting that the encoder is actually a BOW model. This should be discussed in the paper, as it raises concerns about the linguistic interest of the controlled language that was used.
Table 3: indicates is: indicates if