Summary and Contributions: This paper focused on learning a policy for text-based games. The main contributions are two parts: 1) proposed to use knowledge graph to record history, and proposed to divide knowledge graph to sub-graphs to model relational and temporal awareness; 2) proposed hierarchical attention networks, in which the high level attention uses the full knowledge graph and the low level attention uses sub-graphs.
Strengths: POST REBUTTAL: I agree with the other reviewer that the paper has clear empirical gains, but the methodological contribution is not fundamental. Therefore I remain my score of 6. 1. The empirical results show that the proposed sub-graph division and hierarchical attention work well and achieve state-of-the-art performance. 2. The idea of sub-graph division is interesting.
Weaknesses: 1. The paper relies on pre-defined rules (Section 5.2) to divide the full knowledge graph to sub-graphs, which make the paper less interesting. 2. Although the paper has an ablation study, it does not contain enough empirical study and discussion about sub-graphs. For example, in Section 4.2, the authors mentioned that there are two type of sub-graphs, one with historical information and one does not. Which one contributes the most to the final performance? There are two awareness mentioned in Section 4.2 (temporal-awareness and relational awareness), which one contributes the most to the final performance, and why? What exactly is missing from full knowledge graph that sub-graph captures? There should be more discussion about why sub-graphs and low-level attention helps.
Relation to Prior Work: yes
Summary and Contributions: This paper studies the creation of RL agents for text-based games and specifically proposes a stacked hierarchical attention-based knowledge graph to allow the agent to reason about these games. In contrast to previous work in this domain that used knowledge graphs, the proposed approach has better performance due to the mutli-level and multi-modal reasoning. This approach is validated on a variety of man-made text games and shows promising improvements over existing agents.
Strengths: This work extends the KG-A2C agent in several ways: First it uses attention to re-weight the different components of textual observations (e.g. narrative, inventory, location description, and previous action text). The output of this first attention is then combined with another attention computed over multiple different sub-knowledge graphs corresponding to the connectivity of locations, objects in the current location, inventory, and anything that is connected to the current player. While none of the individual building blocks are particularly novel, the combination of all of these elements introduces a lot of flexibility to structurally decompose the different types of knowledge available in the game and allow the agent to pay attention to specific subsets of this knowledge. This flexibility pays dividends when it comes to the experimental evaluation and where this agent significantly improves on KG-A2C in nearly every game. The ablations presented validate that the full stacked architecture is indeed needed to maintain current levels of performance, and the analysis shows that the attention mechansims are working well insofar as they distribute attention correctly between locations descriptions and inventory contents as needed to generate the action. Overall I'm most impressed by the nearly universal improvements achieved by this method.
Weaknesses: While the improvements in game score are undeniable, I find it harder to understand why the SHA-KG architecture is leading to higher scores. Possible hypotheses are the multiple sub-KGs, the attention based processing of observations, the two levels of hierarchy, or some combination herein. While ablations give the idea that the overall architecture is necessary, it's hard to distill which parts (e.g. relational awareness vs historical awareness) are actually leading to better performance and how future agents can build or extend this architecture.
Correctness: As far as I can tell.
Clarity: The paper suffers from minor grammar mistakes, which could hopefully be addressed given further time.
Relation to Prior Work: Yes
Additional Feedback: No source code is provided - so it may be difficult to exactly reproduce the agent. However the detailed equations should be a pretty good guide. After reading the rebuttal and in light of the reviewer discussion - I still think this paper is above acceptance threshold and maintain my original scoring.
Summary and Contributions: The paper presents an approach to playing text-based games using knowledge graph. Previous works show how to use KGs to deal with partial observability, large action spaces, etc. and this work focuses on giving the agent the ability to reason over the KG and input text descriptions with a stacked hierarchical attention mechanism.
Strengths: 1. The paper presents a contribution that directly fixes an inherent limitation in the work that it is based on (the KG-A2C) and other works using KGs for text games – the agents are not using the graph to its fullest potential. 2. The inspiration to use techniques from VQA and treat the KG as another modality is well explained and validated. 3. Results show good improvement over the current state of the art across a suite of games. An ablation study validates most of the architecture choices and provides evidence for both layers of the hierarchy.
Weaknesses: 1. There’s some controversy over whether attention values actually mean anything when applied to text and similar arguments apply to knowledge graphs as well (see “Attention is Not Explanation” https://arxiv.org/abs/1902.10186 NAACL-19 and “Attention is Not Not Explanation” https://arxiv.org/abs/1908.04626 EMNLP-19). Given this and the author’s claims regarding SHA-KG “reasoning” and being interpretable, I do not think that current evidence sufficiently backs the former claim at least. 1a. I would further suggest a reframing of the writing to reflect that this is faithful interprebility at most, i.e. pointing out likely places in text/KG where the action is made (see this for more details https://dl.acm.org/doi/10.1145/3236009) 2. What exactly is the reasoning happening on the KGs here? Some of the confusion is in part because I do not understand what each of the sub-graphs mean semantically. I see examples in the supplementary but the inter play between the sub-graphs and why it was partitioned in this way is lost on me. Some examples of perhaps which nodes on the graph "light up" when making a certain decision would help here and/or ablations differences in attention with varying numbers of sub-graphs might help here
Correctness: A key component of the original KG-A2C is that the knowledge graph was used to constrain the action space via a “graph mask” – I do not see any mentions of whether this mask was used. Although the mask was effective, some games had better performance without it. If it was used, details such as dropout on the mask, etc. should be given in addition to an ablation of SHA-KG without using the mask. And vice versa, if it wasn’t used, then how does SHA-KG do with it. Without this ablation, it is hard to tell exactly how much of the gain in performance can be attributed to the state representation architecture.
Clarity: The paper is well written overall, but I think some changes can be made to improve understanding. 1. Move some details regarding semantics of the graph partitions to the main paper with an example or two, perhaps at the expense of hyperparameter details. 2. Reformulate some of the claims regarding reasoning/interprebility as I have written before. Minor: line 107 rephrase: “In a …, it requires” -> “Text-based games require”
Relation to Prior Work: The main difference between the base work KG-A2C and this is the architecture that encodes the knowledge graph. This is clearly stated and choices for this architecture are explained.
Additional Feedback: The paper contributes, in my opinion, one main useful thing - it shows how to adapt architectures from neighboring fields such as vision&language to treat KGs as an extra modality in traditionally text-only settings. It is a simple but effective idea with clear empirical gains and fills in a short coming of KG-A2C. The main issue I have with this work is with respect to their claims of interpretability, sure you can highlight a few attention values from the GATs but you cannot make any claims regarding "reasoning" just from that as they seem to do. They also do not position their paper and terminology used with respect to all the other work in terms of interpretability/explainability. I put in a few pointers in my original review and the authors said they'd cite it but without seeing what the rewrite of that section looks like, its hard to tell. For these reasons, I will maintain my score of 6 after reading the rebuttal.
Summary and Contributions: This paper describes an approach for solving text-based games. Its main contribution is to use knowledge graph reasoning to address this task. The paper describes a two-step attention mechanism that reasons over subgraphs of the knowledge graph independently. The system and baselines are evaluated on several challenging text-based games.
Strengths: The proposed method is described clearly and shows obvious improvements over existing baselines. The paper also describes some interpretability analysis of the proposed method on several games using the attention mechanism.
Weaknesses: The main concern I have with the proposed approach is its extensibility. How could the use of a knowledge graph extend to more continuous and realistic environments, such as games that include visual observations? And how much of a problem was error propagation in the evaluation of this system (e.g., errors made by OpenIE -- it would be good to have some quantitative analysis, like sampling of extracted triples, to get a feel of how well OpenIE works on these games)? After the rebuttal and discussion, I still think positively of the paper. However, I am also taking into account and agree with the points other reviewers brought up about discussing more the interpretability and reasoning aspects.
Correctness: The system is evaluated on a difficult set of text-based games. There were a few questions I had about the evaluation: * What does "maximum score" mean? Is that like the maximum possible score in the game? * Are different systems trained separately on the 20 games, or is a single system expected to multi-task across games? Regardless, it would be interesting to see a multitasking setup where the system is trained on one subset of the games and tested on another set.
Clarity: The formal definition of the model is very clear. A few minor suggestions: * In the abstract, "explicit reasoning with *a* knowledge graph" sounds a bit better to me because it's not clear what "the knowledge graph" refers to until I've read the intro. * I didn't understand the point made on line 40-41 (last sentence) * In line 135, shouldn't s_t be o_t (observation)? Maybe I'm misunderstanding but s is the true state, right? So the system should only be encoding the observation * What is a "component" of a textual observation? (L150) This became clearer later in the paper, but not super clear without closely knowing the evaluation data. * I'm not very familiar with text-based games, but are the games referenced in  really not made by humans? Or is it that Jericho includes games that are intended for humans to play, rather than games designed for evaluating RL agents? If so, probably "man-made" is a bit misleading and something like "developed for humans to play" would be more accurate. * Would be good to give some context about the three example games in Figure 3 / Section 5.5 -- like what are the general themes/contexts of the games?
Relation to Prior Work: I am not aware of any work that is missing, although I am not very familiar with environments/systems specifically for text-based games.
Additional Feedback: * Why did you reduce the node embedding dimension from KG-A2C?