BUILDING DYNAMIC KNOWLEDGE GRAPHS FROM TEXT USING MACHINE READING COMPREHENSION
We propose a neural machine reading model that constructs dynamic knowledge graphs from procedural text. It builds these graphs recurrently for each step of the described procedure, and uses them to track the evolving states of participant entities. We harness and extend a recently proposed machine reading comprehension (MRC) model to query for entity states, since these states are generally communicated in spans of text and MRC models perform well in extracting entity-centric spans. The explicit, structured, and evolving knowledge graph representations that our model constructs can be used in downstream question answering tasks to improve machine comprehension of text, as we demonstrate empirically. On two comprehension tasks from the recently proposed PROPARA dataset , our model achieves state-of-the-art results. The model also outperforms previous approaches on the RECIPES dataset (Kiddon et al., 2015), which suggests it may apply broadly to procedural text. Finally, we present some evidence that the model's graphical representations help it to impose commonsense constraints on its predictions.
The paper proposes a recurrent knowledge graph (bipartite graph between entities and location nodes) construction & updating mechanism for entity state tracking datasets such as (two) ProPara tasks and Recipes. The model goes through the following three steps: 1) it reads a sentence at each time step t and identifies the location of each entity via machine reading comprehension model such as DrQA (entities are predefined). 2) Co-reference module adjusts relationship scores (soft adjacency matrix) among nodes, including possibly new nodes introduced by the MRC model. 3) to propagate the relational information across all the nodes, the model performs L layers of LSTM for each entity that attend on other nodes via attention (where the weights come from the adjacency matrix). The model repeats the three steps for each sentence. The model is trained by directly supervising for the correct span by the MRC model at each time step, which is possible because the data provides strong supervision for each sentence (not just the answer at the end).
 The model achieves the state of the art in the two tasks of ProPara and Recipes dataset.
Strengths: The paper provides an elegant solution for tracking relationship between entities as time (sentence) progresses. I also agree with the authors that this line of work (dynamic KG construction and modification) is an important area of research. While the model shares a similar spirit to EntNet, I think the model has enough distinctions / contributions, especially given that it outperforms EntNet by a large margin. The model also obtains non-trivial improvement over previous SOTA models.
Weaknesses: Paper could have been written better. I had hard time understanding it. The notations are overall confusing and not explained well. Also there are a few unclear parts which I discuss in questions below.
Questions: 
1. Are e_{i,t} and lambda_{i,t} vectors? Scalars? Abstract node notations? It is not clear in the model section. Also, it took me a long time to figure out that ‘i’ is used to index each entity (it is mentioned later).
2. The paper says v_i (initial representation of each entity) is obtained by looking at the contextualized representations (LSTM outputs) of entity mention in the context. What happens if there are multiple mentions in the text? Which one does it look at?
3. For the LSTM in the graph update, why does it have only one input? Shouldn’t it have two inputs, one for previous hidden state and the other for input?
4. Regarding Recipe experiments, the paper says it reaches a better performance than the baseline using just 10k examples out of 60k. This is great, but could you also report the number when the full dataset is used?
5. What does it mean that in training time the model “updates” the location node representation with the encoding of correct span. Do you mean you use the encoding instead?
6. For ProPara task 2, what threshold did you choose to obtain the P/R/F1 score? Is it the threshold that maximizes F1?