In a task-oriented dialogue system, the dialogue manager policy module predicts actions usually in terms of dialogue acts and domain specific slots. It is a crucial component that influences the efficiency (e.g., the conciseness and smoothness) of the communication between the user and the agent. Both supervised learning (SL) BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 and reinforcement learning (RL) approaches BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9 have been adopted to learn policies. SL learns a policy to predict acts given the dialogue state. Recent work BIBREF10, BIBREF11 also used SL as pre-training for RL to mitigate the sample inefficiency of RL approaches and to reduce the number of interactions. Sequence2Sequence (Seq2Seq) BIBREF12 approaches have also been adopted in user simulators to produce user acts BIBREF13. These approaches typically assume that the agent can only produce one act per turn through classification. Generating only one act per turn significantly limits what an agent can do in a turn and leads to lengthy dialogues, making tracking of state and context throughout the dialogue harder. An example in Table TABREF3 shows how the agent can produce both an inform and a multiple_choice act, reducing the need for additional turns. The use of multiple actions has previously been used in interaction managers that keep track of the floor (who is speaking right now) BIBREF14, BIBREF15, BIBREF16, but the option of generating multiple acts simultaneously at each turn for dialogue policy has been largely ignored, and only explored in simulated scenarios without real data BIBREF17.This task can be cast as a multi-label classification problem (if the sequential dependency among the acts is ignored) or as a sequence generation one as shown in Table TABREF4.In this paper, we introduce a novel policy model to output multiple actions per turn (called multi-act), generating a sequence of tuples and expanding agents' expressive power. Each tuple is defined as $(\textit {continue}, \textit {act}, \textit {slots})$, where continue indicates whether to continue or stop producing new acts, act is an act type (e.g., inform or request), and slots is a set of slots (names) associated with the current act type. Correspondingly, a novel decoder (Figure FIGREF5) is proposed to produce such sequences. Each tuple is generated by a cell called gated Continue Act Slots (gCAS, as in Figure FIGREF7), which is composed of three sequentially connected gated units handling the three components of the tuple. This decoder can generate multi-acts in a double recurrent manner BIBREF18. We compare this model with baseline classifiers and sequence generation models and show that it consistently outperforms them.The proposed policy network adopts an encoder-decoder architecture (Figure FIGREF5). The input to the encoder is the current-turn dialogue state, which follows BIBREF19's definition. It contains policy actions from the previous turn, user dialogue acts from the current turn, user requested slots, the user informed slots, the agent requested slots and agent proposed slots. We treat the dialogue state as a sequence and adopt a GRU BIBREF20 to encode it. The encoded dialogue state is a sequence of vectors $\mathbf {E} = (e_0, \ldots , e_l)$ and the last hidden state is $h^{E}$. The CAS decoder recurrently generates tuples at each step. It takes $h^{E}$ as initial hidden state $h_0$. At each decoding step, the input contains the previous (continue, act, slots) tuple $(c_{t-1},a_{t-1},s_{t-1})$. An additional vector $k$ containing the number of results from the knowledge base (KB) query and the current turn number is given as input. The output of the decoder at each step is a tuple $(c, a, s)$, where $c \in \lbrace \langle \text{continue} \rangle , \langle \text{stop} \rangle , \langle \text{pad} \rangle \rbrace $, $a \in A$ (one act from the act set), and $s \subset S$ (a subset from the slot set).As shown in Figure FIGREF7, the gated CAS cell contains three sequentially connected units for outputting continue, act, and slots respectively.The Continue unit maps the previous tuple $(c_{t-1}, a_{t-1}, s_{t-1})$ and the KB vector $k$ into $x_t^c$. The hidden state from the previous step $h_{t-1}$ and $x_t^c$ are inputs to a $\text{GRU}^c$ unit that produces output $g_t^c$ and hidden state $h_t^c$. Finally, $g_t^c$ is used to predict $c_t$ through a linear projection and a $\text{softmax}$.The Act unit maps the tuple $(c_t, a_{t-1}, s_{t-1})$ and the KB vector $k$ into $x_t^a$. The hidden state from the continue cell $h_t^c$ and $x_t^a$ are inputs to a $\text{GRU}^a$ unit that produces output $g_t^a$ and hidden state $h_t^a$. Finally, $g_t^a$ is used to predict $a_t$ through a linear projection and a $\text{softmax}$.The Slots unit maps the tuple $(c_t, a_t, s_{t-1})$ and the KB vector $k$ into $x_t^s$. The hidden state from the act cell $h_t^a$ and $x_t^s$ are inputs to a $\text{GRU}^s$ unit that produces output $g_t^s$ and hidden state $h_t^s$. Finally, $g_t^a$ is used to predict $s_t$ through a linear projection and a $\text{sigmoid}$. Let $z_t^i$ be the $i$-th slot's ground truth.The overall loss is the sum of the losses of the three units: $\mathcal {L} = \mathcal {L}^c + \mathcal {L}^a + \mathcal {L}^s$The experiment dataset comes from Microsoft Research (MSR) . It contains three domains: movie, taxi, and restaurant. The total count of dialogues per domain and train/valid/test split is reported in Table TABREF11. At every turn both user and agent acts are annotated, we use only the agent side as targets in our experiment. The acts are ordered in the dataset (each output sentence aligns with one act). The size of the sets of acts, slots, and act-slot pairs are also listed in Table TABREF11. Table TABREF12 shows the count of turns with multiple act annotations, which amounts to 23% of the dataset. We use MSR's dialogue management code and knowledge base to obtain the state at each turn and use it as input to every model.We evaluate the performance at the act, frame and task completion level. For a frame to be correct, both the act and all the slots should match the ground truth. We report precision, recall, F$_1$ score of turn-level acts and frames. For task completion evaluation, Entity F$_1$ score and Success F$_1$ score BIBREF21 are reported. The Entity F$_1$ score, differently from the entity match rate in state tracking, compares the slots requested by the agent with the slots the user informed about and that were used to perform the KB query. We use it to measure agent performance in requesting information. The Success F$_1$ score compares the slots provided by the agent with the slots requested by the user. We use it to measure the agent performance in providing information.Critical slots and Non-critical slots: By `non-critical', we mean slots that the user informs the system about by providing their values and thus it is not critical for the system to provide them in the output. Table 1 shows an example, with the genre slot provided by the user and the system repeating it in its answer. Critical slots refers to slots that the system must provide like “moviename” in the Table 1 example. Although non-critical slots do not impact task completion directly, they may influence the output quality by enriching the dialogue state and helping users understand the system's utterance correctly. Furthermore, given the same dialog state, utterances offering non-critical slots or not offering them can both be present in the dataset, as they are optional. This makes the prediction of those slots more challenging for the system. To provide a more detailed analysis, we report the precision, recall, F$_1$ score of turn-level for all slots, critical slots and non-critical slots of the inform act.We compare five methods on the multi-act task.Classification replicates the MSR challenge BIBREF19 policy network architecture: two fully connected layers. We replace the last activation from $\text{softmax}$ to $\text{sigmoid}$ in order to predict probabilities for each act-slot pair. It is equivalent to binary classification for each act-slot pair and the loss is the sum of the binary cross-entropy of all of them.Seq2Seq BIBREF12 encodes the dialogue state as a sequence, and decodes agent acts as a sequence with attention BIBREF22.Copy Seq2Seq BIBREF23 adds a copy mechanism to Seq2Seq, which allows copying words from the encoder input.CAS adopts a single GRU BIBREF20 for decoding and uses three different fully connected layers for mapping the output of the GRU to continue, act and slots. For each step in the sequence of CAS tuples, given the output of the GRU, continue, act and slot predictions are obtained by separate heads, each with one fully connected layer. The hidden state of the GRU and the predictions at the previous step are passed to the cell at the next step connecting them sequentially.gCAS uses our proposed recurrent cell which contains separate continue, act and slots unit that are sequentially connected.The classification architecture has two fully connected layers of size 128, and the remaining models have a hidden size of 64 and a teacher-forcing rate of 0.5. Seq2Seq and Copy Seq2Seq use a beam search with beam size 10 during inference. CAS and gCAS do not adopt a beam search since their inference steps are much less than Seq2Seq methods. All models use Adam optimizer BIBREF24 with a learning rate of 0.001.As shown in Table TABREF13, gCAS outperforms all other methods on Entity F$_1$ in all three domains. Compared to Seq2Seq, the performance advantage of gCAS in the taxi and restaurant domains is small, while it is more evident in the movie domain. The reason is that in the movie domain the proportion of turns with multiple acts is higher (52%), while in the other two domains it is lower (30%). gCAS also outperforms all other models in terms of Success F$_1$ in the movie and restaurant domain but is outperformed by the classification model in the taxi domain. The reason is that in the taxi domain, the agent usually informs the user at the last turn, while in all previous turns the agent usually requests information from the user. It is easy for the classification model to overfit this pattern. The advantage of gCAS in the restaurant domain is much more evident: the agent's inform act usually has multiple slots (see example 2 in Table TABREF15) and this makes classification and sequence generation harder, but gCAS multi-label slots decoder handles it easily.Table TABREF14 shows the turn-level acts and frame prediction performance. CAS and gCAS outperform all other models in acts prediction in terms of F$_1$ score. The main reason is that CAS and gCAS output a tuple at each recurrent step, which makes for shorter sequences that are easier to generate compared to the long sequences of Seq2Seq (example 2 in Table TABREF15). The classification method has a good precision score, but a lower recall score, suggesting it has problems making granular decisions (example 2 in Table TABREF15). At the frame level, gCAS still outperforms all other methods. The performance difference between CAS and gCAS on frames becomes much more evident, suggesting that gCAS is more capable of predicting slots that are consistent with the act. This finding is also consistent with their Entity F$_1$ and Success F$_1$ performance.However, gCAS's act-slot pair performance is far from perfect. The most common failure case is on non-critical slots (like `genre' in the example in Table TABREF4): gCAS does not predict them, while it predicts the critical ones (like `moviename' in the example in Table TABREF4).Table TABREF15 shows predictions of all methods from two emblematic examples. Example 1 is a frequent single-act multi-slots agent act. Example 2 is a complex multi-act example. The baseline classification method can predict frequent pairs in the dataset, but cannot predict any act in the complex example. The generated sequences of Copy Seq2Seq and Seq2Seq show that both models struggle in following the syntax. CAS cannot predict slots correctly even if the act is common in the dataset. gCAS returns a correct prediction for Example 1, but for Example 2 gCAS cannot predict `starttime', which is a non-critical slot.Tables TABREF16 and TABREF17 show the results of all slots, critical slots and non-critical slots under the inform act. gCAS performs better than the other methods on all slots in the movie and restaurant domains. The reason why classification performs the best here in the taxi domain is the same as the Success F$_1$. In the taxi domain, the agent usually informs the user at the last turn. The non-critical slots are also repeated frequently in the taxi domain, which makes their prediction easier. gCAS's performance is close to other methods on critical-slots. The reason is that the inform act is mostly the first act in multi-act and critical slots are usually frequent in the data. All methods can predict them well.In the movie and restaurant domains, the inform act usually appears during the dialogue and there are many optional non-critical slots that can appear (see Table TABREF11, movie and restaurant domains have more slots and pairs than the taxi domain). gCAS can better predict the non-critical slots than other methods. However, the overall performance on non-critical slots is much worse than critical slots since their appearances are optional and inconsistent in the data.In this paper, we introduced a multi-act dialogue policy model motivated by the need for a richer interaction between users and conversation agents. We studied classification and sequence generation methods for this task, and proposed a novel recurrent cell, gated CAS, which allows the decoder to output a tuple at each step. Experimental results showed that gCAS is the best performing model for multi-act prediction. The CAS decoder and the gCAS cell can also be used in a user simulator and gCAS can be applied in the encoder. A few directions for improvement have also been identified: 1) improving the performance on non-critical slots, 2) tuning the decoder with RL, 3) text generation from gCAS. We leave them as future work.We would like to express our special thanks to Alexandros Papangelis and Gokhan Tur for their support and contribution. We also would like to thank Xiujun Li for his help on dataset preparation and Jane Hung for her valuable comments. Bing Liu is partially supported by the NSF grant IIS-1910424 and a research gift from Northrop Grumman.

Q: What datasets are used for training/testing models? 
A: Microsoft Research dataset containing movie, taxi and restaurant domains.

Q: How better is gCAS approach compared to other approaches?
A: For entity  F1 in the movie, taxi and restaurant domain it results in scores of 50.86, 64, and 60.35. For success, it results it outperforms in the movie and restaurant domain with scores of 77.95 and 71.52

Q: What is specific to gCAS cell?
A: It has three sequentially connected units to output continue, act and slots generating multi-acts in a doble recurrent manner.