Recursive Template-based Frame Generation for Task Oriented Dialog (2020)

Abstract: The Natural Language Understanding (NLU) component in task oriented dialog systems processes a user’s request and converts it into structured information that can be consumed by downstream components such as the Dialog State Tracker (DST). This information is typically represented as a semantic frame that captures the intent and slot-labels provided by the user. We first show that such a shallow representation is insufficient for complex dialog scenarios, because it does not capture the recursive nature inherent in many domains. We propose a recursive, hierarchical frame-based representation and show how to learn it from data. We formulate the frame generation task as a template-based tree decoding task, where the decoder recursively generates a template and then fills slot values into the template. We extend local tree-based loss functions with terms that provide global supervision and show how to optimize them end-to-end. We achieve a small improvement on the widely used ATIS dataset and a much larger improvement on a more complex dataset we describe here.

## introduction the output of an nlu component is called a semantic or dialog frame @xcite . the frame consists of intents which capture information about the goal of the user and slot-labels which capture constraints that need to be satisfied in order to fulfill the users' request. for example, in figure 1 , the intent is to book a flight (atis flight) and the slot labels are the from location, to location and the date. the intent detection task can be modeled as a classification problem and slot labeling as a sequential labeling problem. the atis (airline travel information system) dataset @xcite is widely used for evaluating the nlu component. we focus on complex aspects of dialog that occur in real-world scenarios but are not captured in atis or other alternatives such as, dstc @xcite or snips foot_0 . as an example, consider a reasonable user utterance, "can i get two medium veggie pizza and one small lemonade" (figure 2a ). the intent is orderitems. there are two items mentioned, each with three properties. the properties are the name of the item (veggie pizza, lemonade), the quantity of the item (two, one) and size of the item (medium, small). these properties need to be grouped together accurately to successfully fulfill the customer's request -the customer would not be happy with one small veggie pizza. this structure occurs to a limited extent in the atis dataset (figure 2b ), which has specific forms such as, from loc.city name and to loc.city name, which must be distinguished. however, the scale is small enough that these can be separate labels and multi-class slot-labeling approaches that predict each specific form as a separate class (figure 1 ) have had success. in more open domains, this hierarchy-to-multi-class conversion increases the number of classes exponentially vs. an approach that appropriately uses available structure. further, hierarchical relationships, e.g. between fromloc and city name, are ignored, which limits the sharing of data and statistical strength across labels. the contributions of this paper are as follows: ## related work encoder-decoder architectures, e.g. seq2seq models @xcite , are a popular class of approaches to the problem of mapping source sequences (here words) to target sequences (here slot labels) of variable length. seq2seq models have been used to generate agent responses without the need for intermediate dialog components such as the dst or the natural language generator @xcite . however, there has not been much work that uses deeper knowledge of semantic representations in task-oriented dialog. a notable exception is recent work by @xcite , who used a hierarchical representation for dialog that can be easily parsed by off-the-shelf constituency-based parsers. neural constituency parsers @xcite work directly off the input sentence, and as a result, different sentences with the same meaning end up having different syntactic structures. example: "from pittsburgh i'd like to travel to atlanta on september fourth" ( atis_flight ( fromloc ( city_name ( pittsburgh ) ) toloc ( city_name ( atlanta) ) depart_date ( month ( september ) day ( fourth ) ) ) ) { "atis_flight":{ "fromloc":{ "city_name" : "pittsburgh" }, "toloc":{ "city_name" : "atlanta" }, "depart_date":{ "month_name" : "september", "day_number" : "fourth" } } } we define a recursive, hierarchical, frame-based representation allows us to exploit some of the structure in natural language while allowing endto-end training. our template-based generation is similar to sketch-based seq2tree decoding @xcite developed for sql query generation, where the decoder predicts a rough sketch of the meaning, omitting low-level details such as arguments and variable names. here, we generate templates that generalize slot values by their labels. ## proposed approach we learn to map a user's utterance @xmath0, x 2 , ...x n } to a template-based tree representation (figure 2 ), specifically the bracketed representation in figure 3 . we denote the symbols in the bracketed representation by @xmath1, y 2 , ..y m }. the translation from x to y is performed using four components that are jointly trained end-to-end, (1) an encoder, (2) a slot decoder, (3) a tree decoder (figure 4 ) and (4) a pointer network. each of these components is briefly explained below. ## conclusion and future work: with this preliminary work, we showed cases where traditional flat semantic representations fail to capture slot label dependencies and we highlighted the need for deep hierarchical semantic representations for dialog frames. the proposed recursive, hierarchical frame-based representation captures complex relationships between slots labels. we also proposed an approach using a templatebased tree decoder to generate these hierarchical representations from users' utterances. we also introduced global supervision by extending the treebased loss function, and showed that it is possible to learn all this end-to-end. as future work, we are extending the proposed approach and test its efficacy on real human conversations. more broadly, we continue to explore strategies that combine semantic parsing and neural networks for frame generation.