Domain specific goal-oriented dialogue systems typically require modeling three types of inputs, viz., (i) the knowledge-base associated with the domain, (ii) the history of the conversation, which is a sequence of utterances and (iii) the current utterance for which the response needs to be generated. While modeling these inputs, current state-of-the-art models such as Mem2Seq typically ignore the rich structure inherent in the knowledge graph and the sentences in the conversation context. Inspired by the recent success of structure-aware Graph Convolutional Networks (GCNs) for various NLP tasks such as machine translation, semantic role labeling and document dating, we propose a memory augmented GCN for goal-oriented dialogues. Our model exploits (i) the entity relation graph in a knowledge-base  and (ii) the dependency graph associated with an utterance to compute richer representations for words and entities. Further, we take cognizance of the fact that in certain situations, such as, when the conversation is in a code-mixed language, dependency parsers may not be available. We show that in such situations we could use the global word co-occurrence graph and use it to enrich the representations of utterances. We experiment with the modified DSTC2 dataset and its recently released code-mixed versions in four languages and show that our method outperforms existing state-of-the-art methods, using a wide range of evaluation metrics. Goal-oriented dialogue systems which can assist humans in various day-to-day activities have widespread applications in several domains such as e-commerce, entertainment, healthcare, etc. For example, such systems can help humans in scheduling medical appointments, reserving restaurants, booking tickets, etc.. From a modeling perspective, one clear advantage of dealing with domain specific goal-oriented dialogues is that the vocabulary is typically limited, the utterances largely follow a fixed set of templates and there is an associated domain knowledge which can be exploited. More specifically, there is some structure associated with the utterances as well as the knowledge base. More formally, the task here is to generate the next response given (i) the previous utterances in the conversation history (ii) the current user utterance (known as the query) and (iii) the entities and relationships in the associated knowledge base. Current state-of-the-art methods BID30 BID23 typically use variants of Recurrent Neural Network BID10 to encode the history and current utterance and an external memory network to store the entities in the knowledge base. The encodings of the utterances and memory elements are then suitably combined using an attention network and fed to the decoder to generate the response, one word at a time. However, these methods do not exploit the structure in the knowledge base as defined by entity-entity relations and the structure in the utterances as defined by a dependency parse. Such structural information can be exploited to improve the performance of the system as demonstrated by recent works on syntax-aware neural machine translation BID13 BID2 BID4 , semantic role labeling and document dating BID35 which use GCNs BID8 BID9 BID19 to exploit sentence structure.In this work, we propose to use such graph structures for goal-oriented dialogues. In particular, we compute the dependency parse tree for each utterance in the conversation and use a GCN to capture the interactions between words. This allows us to capture interactions between distant words in the sentence as long as they are connected by a dependency relation. We also use GCNs to encode the entities of the KB where the entities are treated as nodes and the relations as edges of the graph. Once we have a richer structure aware representation for the utterances and the entities, we use a sequential attention mechanism to compute an aggregated context representation from the GCN node vectors of the query, history and entities. Further, we note that in certain situations, such as, when the conversation is in a code-mixed language or a language for which parsers are not available then it may not be possible to construct a dependency parse for the utterances. To overcome this, we construct a co-occurrence matrix from the entire corpus and use this matrix to impose a graph structure on the utterances. More specifically, we add an edge between two words in a sentence if they co-occur frequently in the corpus. Our experiments suggest that this simple strategy acts as a reasonable substitute for dependency parse trees.We perform experiments with the modified DSTC2 BID3 dataset which contains goal-oriented conversations for reserving restaurants. We also use its recently released code-mixed versions BID1 which contain code-mixed conversations in four different languages, viz., Hindi, Bengali, Gujarati and Tamil. We compare with recent state-of-the-art methods and show that on average the proposed model gives an improvement of 2.8 BLEU points and 2 ROUGE points.Our contributions can be summarized as follows: (i) We use GCNs to incorporate structural information for encoding query, history and KB entities in goal-oriented dialogues (ii) We use a sequential attention mechanism to obtain query aware and history aware context representations (iii) We leverage co-occurrence frequencies and PPMI (positive-pointwise mutual information) values to construct contextual graphs for code-mixed utterances and (iv) We show that the proposed model obtains state-of-the-art results on the modified DSTC2 dataset and its recently released code-mixed versions. In this section we discuss the results of our experiments as summarized in tables 1,2, and 3. We use BLEU BID28 and ROUGE BID21 metrics to evaluate the generation quality of responses. We also report the per-response accuracy which computes the percentage of responses in which the generated response exactly matches the ground truth response. In order to evaluate the model's capability of correctly injecting entities in the generated response, we report the entity F1 measure as defined in .Results on En-DSTC2 : We compare our model with the previous works on the English version of modified DSTC2 in table 1. For most of the retrieval based models, the BLEU or ROUGE scores are not available as they select a candidate from a list of candidates as opposed to generating it. Our model outperforms all of the retrieval and generation based models. We obtain a gain of 0.7 in the per-response accuracy compared to the previous retrieval based state-of-the-art model of BID30 , which is a very strong baseline for our generation based model. We call this a strong baseline because the candidate selection task of this model is easier than the response generation task of our model. We also obtain a gain of 2.8 BLEU points, 2 ROUGE points and 2.5 entity F1 points compared to current state-of-the-art generation based models.Results on code-mixed datasets and effect of using RNNs: The results of our experiments on the code-mixed datasets are reported in table 2. Our model outperforms the baseline models on all the code-mixed languages. One common observation from the results over all the languages (including En-DSTC2) is that RNN+GCN-SeA performs better than GCN-SeA. Similar observations were made by for the task of semantic role labeling.Effect of using Hops: As we increased the number of hops of GCNs, we observed a decrease in the performance. One reason for such a drop in performance could be that the average utterance length is very small (7.76 words). Thus, there isn't much scope for capturing distant neighbourhood information and more hops can add noisy information. Please refer to Appendix B for detailed results about the effect of varying the number of hops.Frequency vs PPMI graphs: We observed that PPMI based contextual graphs were slightly better than frequency based contextual graphs (See Appendix C). In particular, when using PPMI as opposed to frequency based contextual graph, we observed a gain of 0.95 in per-response accuracy, 0.45 in BLEU, 0.64 in ROUGE and 1.22 in entity F1 score when averaged across all the code-mixed languages.Effect of using Random Graphs: GCN-SeA-Random and GCN-SeA-Structure take the token embeddings directly instead of passing them though an RNN. This ensures that the difference in performance of the two models are not influenced by the RNN encodings. The results are shown in table 3 and we observe a drop in performance for GCN-Random across all the languages. This Table 3 : GCN-SeA with random graphs and frequency co-occurrence graphs on all DSTC2 datasets.shows that any random graph does not contribute to the performance gain of GCN-SeA and the dependency and contextual structures do play an important role.Ablations : We experiment with replacing the sequential attention by the Bahdanau attention BID0 . We also experiment with various combinations of RNNs and GCNs as encoders.The results are shown in We showed that structure aware representations are useful in goal-oriented dialogue and we obtain state-of-the art performance on the modified DSTC2 dataset and its recently released code-mixed versions. We used GCNs to infuse structural information of dependency graphs and contextual graphs to enrich the representations of the dialogue context and KB. We also proposed a sequential attention mechanism for combining the representations of (i) query (current utterance), (ii) conversation history and (ii) the KB. Finally, we empirically showed that when dependency parsers are not available for certain languages such as code-mixed languages then we can use word co-occurrence frequencies and PPMI values to extract a contextual graph and use such a graph with GCNs for improved performance. south part of town. bot api call R cuisine south moderate api call R cuisine south moderate KB Triples: pizza hut cherry hinton R post code pizza hut cherry hinton post code pizza hut cherry hinton R cuisine italian pizza hut cherry hinton R location south pizza hut cherry hinton R phone pizza hut cherry hinton phone pizza hut cherry hinton R address pizza hut cherry hinton address pizza hut cherry hinton R price moderate pizza hut cherry hinton R rating 3 restaurant alimentum R post code restaurant alimentum post code restaurant alimentum R cuisine european restaurant alimentum R location south restaurant alimentum R phone restaurant alimentum phone restaurant alimentum R address restaurant alimentum address restaurant alimentum R price moderate restaurant alimentum R rating 10 user <SILENCE> <SILENCE> bot restaurant alimentum is a nice restaurant in the south of town serving modern european food.
We propose a Graph Convolutional Network based encoder-decoder model with sequential attention for goal-oriented dialogue systems.
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