Question: How is the information extracted?
Evidence: The first phase is to extract a knowledge graph from the story that depicts locations, characters, objects, and the relations between these entities. We present two techniques. The first uses neural question-answering technique to extract relations from a story text. The second, provided as a baseline, uses OpenIE5, a commonly used rule-based information extraction technique. For the sake of simplicity, we considered primarily the location-location and location-character/object relations, represented by the “next to” and “has” edges respectively in Figure FIGREF4.
While many neural models already exist that perform similar tasks such as named entity extraction and part of speech tagging, they often come at the cost of large amounts of specialized labeled data suited for that task. We instead propose a new method that leverages models trained for context-grounded question-answering tasks to do entity extraction with no task dependent data or fine-tuning necessary. Our method, dubbed AskBERT, leverages the Question-Answering (QA) model ALBERT BIBREF15. AskBERT consists of two main steps as shown in Figure FIGREF7: vertex extraction and graph construction.
The first step is to extract the set of entities—graph vertices—from the story. We are looking to extract information specifically regarding characters, locations, and objects. This is done by using asking the QA model questions such as “Who is a character in the story?”. BIBREF16 have shown that the phrasing of questions given to a QA model is important and this forms the basis of how we formulate our questions—questions are asked so that they are more likely to return a single answer, e.g. asking “Where is a location in the story?” as opposed to “Where are the locations in the story?”. In particular, we notice that pronoun choice can be crucial; “Where is a location in the story?” yielded more consistent extraction than “What is a location in the story?”. ALBERT QA is trained to also output a special <$no$-$answer$> token when it cannot find an answer to the question within the story. Our method makes use of this by iteratively asking QA model a question and masking out the most likely answer outputted on the previous step. This process continues until the <$no$-$answer$> token becomes the most likely answer.
The next step is graph construction. Typical interactive fiction worlds are usually structured as trees, i.e. no cycles except between locations. Using this fact, we use an approach that builds a graph from the vertex set by one relation—or edge—at a time. Once again using the entire story plot as context, we query the ALBERT-QA model picking a random starting location $x$ from the set of vertices previously extracted.and asking the questions “What location can I visit from $x$?” and “Who/What is in $x$?”. The methodology for phrasing these questions follows that described for the vertex extraction. The answer given by the QA model is matched to the vertex set by picking the vertex $u$ that contains the best word-token overlap with the answer. Relations between vertices are added by computing a relation probability on the basis of the output probabilities of the answer given by the QA model. The probability that vertices $x,u$ are related:
We compared our proposed AskBERT method with a non-neural, rule-based approach. This approach is based on the information extracted by OpenIE5, followed by some post-processing such as named-entity recognition and part-of-speech tagging. OpenIE5 combines several cutting-edge ideas from several existing papers BIBREF17, BIBREF18, BIBREF19 to create a powerful information extraction tools. For a given sentence, OpenIE5 generates multiple triples in the format of $\langle entity, relation, entity\rangle $ as concise representations of the sentence, each with a confidence score. These triples are also occasionally annotated with location information indicating that a triple happened in a location.
As in the neural AskBERT model, we attempt to extract information regarding locations, characters, and objects. The entire story plot is passed into the OpenIE5 and we receive a set of triples. The location annotations on the triples are used to create a set of locations. We mark which sentences in the story contain these locations. POS tagging based on marking noun-phrases is then used in conjunction with NER to further filter the set of triples—identifying the set of characters and objects in the story.
The graph is constructed by linking the set of triples on the basis of the location they belong to. While some sentences contain very explicit location information for OpenIE5 to mark it out in the triples, most of them do not. We therefore make the assumption that the location remains the same for all triples extracted in between sentences where locations are explicitly mentioned. For example, if there exists $location A$ in the 1st sentence and $location B$ in the 5th sentence of the story, all the events described in sentences 1-4 are considered to take place in $location A$. The entities mentioned in these events are connected to $location A$ in the graph.
The first phase is to extract a knowledge graph from the story that depicts locations, characters, objects, and the relations between these entities. We present two techniques. The first uses neural question-answering technique to extract relations from a story text. The second, provided as a baseline, uses OpenIE5, a commonly used rule-based information extraction technique. For the sake of simplicity, we considered primarily the location-location and location-character/object relations, represented by the “next to” and “has” edges respectively in Figure FIGREF4.
We instead propose a new method that leverages models trained for context-grounded question-answering tasks to do entity extraction with no task dependent data or fine-tuning necessary. Our method, dubbed AskBERT, leverages the Question-Answering (QA) model ALBERT BIBREF15. AskBERT consists of two main steps as shown in Figure FIGREF7: vertex extraction and graph construction.
The first step is to extract the set of entities—graph vertices—from the story. We are looking to extract information specifically regarding characters, locations, and objects. This is done by using asking the QA model questions such as “Who is a character in the story?”. BIBREF16 have shown that the phrasing of questions given to a QA model is important and this forms the basis of how we formulate our questions—questions are asked so that they are more likely to return a single answer, e.g. asking “Where is a location in the story?” as opposed to “Where are the locations in the story?”. In particular, we notice that pronoun choice can be crucial; “Where is a location in the story?” yielded more consistent extraction than “What is a location in the story?”. ALBERT QA is trained to also output a special <$no$-$answer$> token when it cannot find an answer to the question within the story. Our method makes use of this by iteratively asking QA model a question and masking out the most likely answer outputted on the previous step. This process continues until the <$no$-$answer$> token becomes the most likely answer.
The first step is to extract the set of entities—graph vertices—from the story. We are looking to extract information specifically regarding characters, locations, and objects. This is done by using asking the QA model questions such as “Who is a character in the story?”. BIBREF16 have shown that the phrasing of questions given to a QA model is important and this forms the basis of how we formulate our questions—questions are asked so that they are more likely to return a single answer, e.g. asking “Where is a location in the story?” as opposed to “Where are the locations in the story?”. In particular, we notice that pronoun choice can be crucial; “Where is a location in the story?” yielded more consistent extraction than “What is a location in the story?”. ALBERT QA is trained to also output a special <$no$-$answer$> token when it cannot find an answer to the question within the story. Our method makes use of this by iteratively asking QA model a question and masking out the most likely answer outputted on the previous step. This process continues until the <$no$-$answer$> token becomes the most likely answer.

The next step is graph construction. Typical interactive fiction worlds are usually structured as trees, i.e. no cycles except between locations. Using this fact, we use an approach that builds a graph from the vertex set by one relation—or edge—at a time. Once again using the entire story plot as context, we query the ALBERT-QA model picking a random starting location $x$ from the set of vertices previously extracted.and asking the questions “What location can I visit from $x$?” and “Who/What is in $x$?”. The methodology for phrasing these questions follows that described for the vertex extraction. The answer given by the QA model is matched to the vertex set by picking the vertex $u$ that contains the best word-token overlap with the answer. Relations between vertices are added by computing a relation probability on the basis of the output probabilities of the answer given by the QA model.
We compared our proposed AskBERT method with a non-neural, rule-based approach. This approach is based on the information extracted by OpenIE5, followed by some post-processing such as named-entity recognition and part-of-speech tagging. OpenIE5 combines several cutting-edge ideas from several existing papers BIBREF17, BIBREF18, BIBREF19 to create a powerful information extraction tools. For a given sentence, OpenIE5 generates multiple triples in the format of $\langle entity, relation, entity\rangle $ as concise representations of the sentence, each with a confidence score. These triples are also occasionally annotated with location information indicating that a triple happened in a location.

As in the neural AskBERT model, we attempt to extract information regarding locations, characters, and objects. The entire story plot is passed into the OpenIE5 and we receive a set of triples. The location annotations on the triples are used to create a set of locations. We mark which sentences in the story contain these locations. POS tagging based on marking noun-phrases is then used in conjunction with NER to further filter the set of triples—identifying the set of characters and objects in the story.

The graph is constructed by linking the set of triples on the basis of the location they belong to. While some sentences contain very explicit location information for OpenIE5 to mark it out in the triples, most of them do not. We therefore make the assumption that the location remains the same for all triples extracted in between sentences where locations are explicitly mentioned. For example, if there exists $location A$ in the 1st sentence and $location B$ in the 5th sentence of the story, all the events described in sentences 1-4 are considered to take place in $location A$. The entities mentioned in these events are connected to $location A$ in the graph.
Answer: The answers are shown as follows:
* neural question-answering technique to extract relations from a story text
* OpenIE5, a commonly used rule-based information extraction technique