AGIF : An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling (2020)

Abstract: In real-world scenarios, users usually have multiple intents in the same utterance. Unfortunately, most spoken language understanding (SLU) models either mainly focused on the single intent scenario, or simply incorporated an overall intent context vector for all tokens, ignoring the fine-grained multiple intents information integration for token-level slot prediction. In this paper, we propose an Adaptive Graph-Interactive Framework (AGIF) for joint multiple intent detection and slot filling, where we introduce an intent-slot graph interaction layer to model the strong correlation between the slot and intents. Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information, making a fine-grained intent information integration for the token-level slot prediction. Experimental results on three multi-intent datasets show that our framework obtains substantial improvement and achieves the state-of-the-art performance. In addition, our framework achieves new state-of-the-art performance on two single-intent datasets.

## introduction spoken language understanding (slu) @xcite is a core component of task-oriented dialog systems. it consists of two typical subtasks, intent detection and slot filling @xcite . take the utterance "please play happy birthday" for example, the intent detection can be seen as a classification task to classify the intent label (i.e., playmusic) while the slot filling can be treated as a sequence labeling task to predict the slot label sequence (i.e., o, o, b-music, i-music). dominant slu systems in the literature @xcite @xcite @xcite adopt joint * email corresponding. models to model the relation between the two tasks, which is a direction we follow. though achieving promising performances, most prior work only focus on the simple single intent scenario. their models are trained based on the assumption that each utterance only has one single intent. actually, users usually express multiple intents in an utterance and @xcite shows that 52% of examples are multi-intent in the amazon internal dataset. nevertheless, the existing trained single intent slu models fail to effectively handle the multi-intent settings with the original network structure. ideally, when an slu system meets an utterance with multiple intents, as shown in figure 1 (a), the model should directly detect its all intents (playmusic and getweather). hence, it is important to consider multi-intent slu. unlike the prior single intent slu model which can simply leverage the utterance's single intent to guide slot prediction @xcite , multi-intent slu faces to multiple intents and presents a unique challenge that is worth studying: how to effectively incorporate multiple intents information to lead the slot prediction. to this end, @xcite first explored the multi-task framework with the slot-gated mechanism @xcite for joint multiple intent detection and slot filling. their model incorporated intent information by simply treating an intent context vector as multiple intents information. while this is a direct method for incorporating multiple intents information, it does not offer fine-grained intent information integration for token-level slot filling in the sense that each token is guided with the same complex intents information, which is shown in figure 1(a ). in addition, providing the same intent information for all tokens may introduce ambiguity, where it's hard for each token to capture the related intent information. as please play happy birthday and what is the weather forecast in deepwater bonaire o o b-music i-music o o o o o o o b-city b-country playmusic getweather (a) please play happy birthday and what is the weather forecast in deepwater bonaire o o b-music i-music o o o o o o o b-city b-country playmusic getweather shown in figure 1 (b), these tokens "happy birthday" should focus on the intent "playmusic" while tokens "deepwater bonaire" depend on the intent "getweather". thus, each token should focus on the corresponding intent and it's critical to make a fine-grained intent information integration for the token-level slot prediction. in this paper, we propose an adaptive graph-interactive framework (agif) to address the aforementioned concern. the core module is the proposed adaptive intent-slot graph interaction layer, which is constructed of each token's hidden state of slot filling decoder and embeddings of predicted multiple intents. in this graph, each token's slot node directly connects all predicted intent nodes to explicitly build the correlation between slot and intents. such an interaction graph is applied to each token adaptively, which make each token has the ability to capture different relevant intent information so that fine-grained multiple intents integration can be achieved. in contrast to prior work simply incorporate multiple intents information statically where the same intents information is used for guiding all tokens, our intent-slot interaction graph is constructed adaptively with graph attention network over each token. this encourages our model to automatically filter the irrelevant information and capture important intent at the token-level. we first conduct experiments on the multi-intent benchmark dataset dstc4 @xcite . then, to verify the generalization of our framework, we empirically construct two large-scale multiintent datasets @xcite and mixsnips @xcite . the results of these experiments show the effectiveness of our framework by outperforming the current state-of-the-art method. to the best of our knowledge, there are no public large-scale multiple intents datasets and we hope the release of it would push forward the research of multi-intent slu. in addition, our framework achieves state-of-the-art performance on two public single-intent datasets including atis @xcite and snips @xcite , which further verifies the generalization of the proposed model. to facilitate future research in this area, all datasets and codes are publicly available at https: //github.com/looperxx/agif. ## approach the architecture of our framework is demonstrated in figure 2 , which consists of a shared encoder, an adaptive intent-slot graph interaction layer and two separate decoders. first, the encoder ( §2.1) uses a shared self-attentive encoder to represent an utterance, which can grasp the shared information between intent detection and slot filling. then, the intent detection decoder ( §2.2) performs the multi-label classification to detect multiple intents. finally, we introduce the adaptive intent-slot graph interaction layer ( §2.3) to explicitly leverage the multiple intents information for guiding slot prediction. both intent detection and slot filling are optimized simultaneously via a multi-task learning scheme. ## related work intent detection intent detection is formulated as an utterance classification problem. different classification methods, such as support vector machine (svm) and rnn @xcite , have been proposed to solve it. @xcite adopts a capsule-based neural network with self-attention for intent detection. however, the above models mainly focus on the single intent scenario, which can not handle the complex multiple intent scenario. @xcite and @xcite explore the complex scenario, where multiple intents are assigned to a user's utterance. @xcite use log-linear models to achieve this, while we use neural network models. compared with their work, we jointly perform multi-label intent detection and slot prediction, while they only consider the subtask intent detection. slot filling slot filling can be treated as a sequence labeling task. the popular approaches are conditional random fields (crf) @xcite and recurrent neural networks (rnn) @xcite . recently, @xcite and @xcite introduce the self-attention mechanism for crf-free sequential labeling. joint model to consider the high correlation between intent and slots, many joint models @xcite @xcite @xcite are proposed to solve two tasks. goo et al. (2018); li et al. (2018); zhang et al. (2019) propose to utilize the intent information to guide the slot filling. qin et al. (2019) further utilize a stack-propagation framework for better leveraging intent semantic information to guide the slot filling, which achieves the state-of-the-art performance. wang et al. (2018) and e et al. (2019) consider the cross-impact between the slot and intents. our framework follows those state-of-the-art joint model paradigm, and further focus on the multiple intents scenario while the above joint models do not consider. recently, gangadharaiah and narayanaswamy ( 2019 ) propose a joint model to consider the multiple intent detection and slot filling simultaneously where they explicitly leverage overall intent information with the gate mechanism to guide all tokens slot prediction. compared with this work, the main differences are as following: 1) our framework exploits a fine-grained intent information transfer with a unified graph interaction architecture while their work simply incorporates the same intents information for all tokens slot prediction. 2) as far as we know, their corpus and code are not distributed, which makes it hard to follow. in contrast, we empirically construct two large-scale multi-intent slu datasets where all datasets and code have been released. we hope it would push forward the research of multiintent slu. ## conclusion in our paper, we propose a token-level adaptive graph-interactive framework to model the interaction between multiple intents and slot at each token, which can make a fine-grained intent information transfer for slot prediction. to our best of knowledge, this is the first work to explore fine-grained intents information transfer in multi-intent slu. in addition, we release two multi-intent datasets and hope it can push forward the research this area. experiments on four datasets show the effectiveness of the proposed models and achieve state-of-the-art performance.