Title: CODRAW: COLLABORATIVE DRAWING AS A TESTBED FOR GROUNDED GOAL-DRIVEN COMMUNICATION Anonymous

Abstract: In this work, we propose a goal-driven collaborative task that contains language, vision, and action in a virtual environment as its core components. Specifically, we develop a Collaborative image-Drawing game between two agents, called CoDraw. Our game is grounded in a virtual world that contains movable clip art objects. The game involves two players: a Teller and a Drawer. The Teller sees an abstract scene containing multiple clip art pieces in a semantically meaningful configuration, while the Drawer tries to reconstruct the scene on an empty canvas using available clip art pieces. The two players communicate via two-way communication using natural language. We collect the CoDraw dataset of ∼10K dialogs consisting of ∼138K messages exchanged between human agents. We define protocols and metrics to evaluate the effectiveness of learned agents on this testbed, highlighting the need for a novel crosstalk condition which pairs agents trained independently on disjoint subsets of the training data for evaluation. We present models for our task, including simple but effective nearest-neighbor techniques and neural network approaches trained using a combination of imitation learning and goal-driven training. All models are benchmarked using both fully automated evaluation and by playing the game with live human agents. Drag & Drop waiting for Drawer's message… 1. You: Ready
In this section, we first detail our task, then present the CoDraw dataset, and finally propose a Scene Similarity Metric which allows automatic evaluation of the reconstructed and original scene.
TASK

1. INTRODUCTION:

Building agents that can interact with humans in natural language while perceiving and taking actions in their environments is one of the fundamental goals in artificial intelligence. One of the required components, language understanding, has traditionally been studied in isolation and with tasks aimed at imitating human behavior (e.g. language modeling Bengio et al. (2003) ; Mikolov et al. (2010) , machine translation Bahdanau et al. (2014) ; Sutskever et al. (2014) , etc.) by learning from large text-only corpora. To incorporate both vision and action, it is important to have the language grounded (Harnad, 1990; Barsalou, 1999) , where words like cat are connected to visual percepts and words like move relate to actions taken in an environment. Additionally, judging language understanding purely based on the ability to mimic human utterances has limitations: there are many ways to express roughly the same meaning, and conveying the correct information is often more important than the particular choice of words. An alternative approach, which has recently gained increased prominence, is to train and evaluate language generation capabilities in an interactive setting, where the focus is on successfully communicating information that an agent must share in order to achieve its goals. In this paper, we propose the Collaborative Drawing (CoDraw) task, which combines grounded language understanding and learning effective goal-driven communication into a single, unified testbed. This task involves perception, communication, and actions in a partially observable virtual environment. As shown in Figure 1 , our game is grounded in a virtual world constructed by clip art objects (Zitnick et al., 2013; Zitnick & Parikh, 2013) . Two players, Teller and Drawer, play the game. The Teller sees an abstract scene made from clip art objects in a semantically meaningful configuration, while the Drawer sees a drawing canvas that is initially empty. The goal of the game is to have both players communicate so that the Drawer can reconstruct the image of the Teller, without ever seeing it.

Our task requires effective communication because the two players cannot see each other's scenes. The Teller has to describe the scene in sufficient detail for the Drawer to reconstruct it, which will require rich grounded language. Moreover, the Drawer will need to carry out a series of actions from a rich action space to position, orient, and resize all of the clip art pieces required for the reconstruction. Note that such actions are only made possible through clip art pieces: they can represent semantically meaningful configurations of a visual scene that are easy to manipulate, in contrast to low-level pixel-based image representations. The performance of a pair of agents is judged based on the quality of reconstructed scenes. We consider high-quality reconstructions as a signal that communication has been successful.

As we develop models and protocols for CoDraw, we found it critical to train the Teller and the Drawer separately on disjoint subsets of the training data. Otherwise, the two machine agents may conspire to successfully achieve the goal while communicating using a shared "codebook" that bears little resemblance to natural language. We call this separate-training, joint-evaluation protocol crosstalk, which prevents learning of mutually agreed upon codebooks, while still checking for goal completion at test time. We highlight crosstalk as one of our contributions, and believe it can be generally applicable to other related tasks (Sukhbaatar et al., 2016; Foerster et al., 2016; de Vries et al., 2016; Das et al., 2017b; Lewis et al., 2017) .

Summary of Contributions:

• We propose a novel CoDraw task, which is a game designed to facilitate the learning and evaluation of effective natural language communication in a grounded context. 

2. RELATED WORK:

Language and Vision. The proposed CoDraw game is related to several well-known language and vision tasks that study grounded language understanding (Karpathy & Fei-Fei, 2015; Donahue et al., 2015; de Vries et al., 2016) . For instance, compared to image captioning (Vinyals et al., 2017; Xu et al., 2015; Chen & Zitnick, 2015; Lu et al., 2017) , visual question answering (Antol et al., 2015; Zhang et al., 2016; Goyal et al., 2016; Gao et al., 2015; Krishna et al., 2017; Malinowski & Fritz, 2014; Ren et al., 2015; Tapaswi et al., 2016; Yu et al., 2015; Zhu et al., 2016) and recent embodied extensions (Das et al., 2018) , CoDraw involves multiple rounds of interactions between two agents. Both agents hold their own partially observable states and need to build a mental model for each other to collaborate. Compared to work on navigation (Vogel & Jurafsky, 2010; Anderson et al., 2018; Fried et al., 2018b) where an agent must follow instructions to move itself in a static environment, CoDraw involves moving and manipulating multiple clip art pieces, which must jointly form a semantically meaningful scene. Compared to visual dialog (Das et al., 2017a; b; Strub et al., 2017; Mostafazadeh et al., 2017) tasks, agents need to additionally cooperate to change the environment with actions (e.g., move around pieces). Thus, the agents have to possess the ability to adapt and hold a dialog about novel scenes that will be constructed as a consequence of their dialog.

In addition, we also want to highlight that CoDraw has a well-defined communication goal, which facilitates objective measurement of success and enables end-to-end goal-driven learning.

End-to-end Goal-Driven Dialog. Traditional goal-driven agents are often based on 'slot filling' (Lemon et al., 2006; Wang & Lemon, 2013; Yu et al., 2015) , in which the structure of the dialog is pre-specified but the individual slots are replaced by relevant information. Recently, endto-end neural models are also proposed for goal-driven dialog (Bordes et al., 2017; Li et al., 2017a; b; He et al., 2017) , as well as goal-free dialog or 'chit-chat' (Shang et al., 2015; Sordoni et al., 2015; Vinyals & Le, 2015; Li et al., 2016; Dodge et al., 2016) . Unlike CoDraw, in these approaches, symbols in the dialog are not grounded into visual objects.

Language Grounded in Environments. Learning language games to change the environment has been studied recently (Wang et al., 2016; 2017) . The agent can change the environment using the grounded natural language. However, agents do not have the need to cooperate. Other work on grounded instruction following relies on datasets of pre-generated action sequences annotated with human descriptions, rather than using a single end goal (Long et al., 2016) . Speaker models for these tasks are only evaluated based on their ability to describe an action sequence that is given to them (Fried et al., 2018a) , whereas Teller models for CoDraw also need to select a desired action sequence in a goal-driven manner. Language grounding has also been studied for robot navigation, manipulation, and environment mapping (Tellex et al., 2011; Mei et al., 2015; Daniele et al., 2016) . However, these works manually pair each command with robot actions and lack end-to-end training (Tellex et al., 2011) , dialog (Mei et al., 2015; Daniele et al., 2016) , or both (Walter et al., 2014) .

Emergent Communication. Building on the seminal works by Lewis (1969; 1975) , a number of recent works study cooperative games between agents where communication protocols emerge as a consequence of training the agents to accomplish shared goals (Sukhbaatar et al., 2016; Foerster et al., 2016) . These methods have typically been applied to learn to communicate small amounts of information, rather than the complete, semantically meaningful scenes used in the CoDraw task.

In addition, the learned communication protocols are usually not natural (Kottur et al., 2017) or interpretable. On the other hand, since our goal is to develop agents that can assist and communicate with humans, we must pre-train our agents on human communication and use techniques that can cope with the greater linguistic variety and richness of meaning present in natural language.

single message is capped at 140 characters: this prevents excessively verbose descriptions and gives the Drawer more chances to participate in the dialog by encouraging the Teller to pause more frequently. Both participants were asked to submit the task when they are both confident that Drawer has accurately reconstructed the scene of Teller. Our dataset, as well as this infrastructure for live chat with live drawing, will be made publicly available.

Additional Interaction. We did not allow Teller to continuously observe Drawer's canvas to make sure that the natural language focused on the high-level semantics of the scene rather than instructions calling for the execution of low-level clip art manipulation actions, but we hypothesize that direct visual feedback may be necessary to get the all the details right. For this, we give one chance for the Teller to look at the Drawer's canvas using a 'peek' button in the interface. Communication is only allowed after the peek window is closed.

3.2. DATASET:

We collect 9,993 The message length distribution for the Drawer is skewed toward 1 with the passive replies like "ok", "done", etc. There does exist a heavy tail, which shows that Drawers do ask clarifying questions about the scene like "where is trunk of second tree, low or high". On the other hand, the distribution of number of tokens in Tellers' utterances is relatively smooth with long tails. The vocabulary size is 4,555. Since the subject of conversations is about abstract scenes with a limited number of clip arts, the vocabulary is relatively small compared to those on real images. See Appendix B for a more detailed analysis of our dataset, where we study the lengths of the conversations, the number of rounds, and the distributions of scene similarity scores when humans perform the task.

3.3. SCENE SIMILARITY METRIC:

The goal-driven nature of our task naturally lends itself to evaluation by measuring the similarity of the reconstructed scene to the original. For this purpose we define a scene similarity metric, which allows us to automatically evaluate communication effectiveness both at the end of a dialog and at intermediate states. We use the metric to compare how well different machine-machine, humanmachine, and human-human pairs can complete the CoDraw task.

Let c i , c j denote the identity, location, configuration of two clipart pieces i and j. A clipart image C = {c i } is then simply a set of clipart pieces. Given two images C and Ĉ, we compute scene similarity by first finding the common clipart pieces C ∩ Ĉ and then computing unary f (c i ) and pairwise terms g(c i , c j ) on these pieces in common:

s(C, Ĉ) = c∈C∩ Ĉ f (c) |C ∪ Ĉ| unary + ci,cj ∈C∩ Ĉ,i<j g(c i , c j ) |C ∪ Ĉ|(|C ∩ Ĉ| -1) pairwise (1)

Using f (c) = 1 and g(c i , c j ) = 0 would result in the standard intersection-over-union measure used for scoring set predictions. The denominator terms normalize the metric to penalize missing or extra clip art, and we set f and g such that our metric is on a 0-5 scale. The exact terms f and g are described in Appendix C.

4. MODELS:

We model both the Teller and the Drawer, and evaluate the agents using the metrics described in the previous section. Informed by our analysis of the collected dataset (see Appendix B), we make three modeling assumptions compared to the full generality of the setup that humans were presented with during data collection. These assumptions hold for all models studied in this paper.

Assumption 1: Silent Drawer. We choose to omit the Drawer's ability to ask clarification questions; instead, our Drawer models will always answer "ok" and our Teller models will not condition on the text of the Drawer replies. This is consistent with typical human replies (around 62% of which only use a single token) and the fact that the Drawer talking is not strictly required to resolve the information asymmetry inherent in the task. We note that this assumption does not reduce the number of modalities needed to solve the task: there is still language generation on the Teller side, in addition to language understanding, scene perception, and scene generation on the Drawer side. Drawer models that can detect when a clarification is required, and then generate a natural language clarification question is interesting future work.

Assumption 2: No Peek Action. The second difference is that the data collection process for humans gives the Teller a single chance to peek at the Drawer's canvas, a behavior we omit from our models. Rich communication is still required without this behavior, and omitting it also does not decrease the number of modalities needed to complete the task. We leave for future work the creation of models that can peek at the time that maximizes task effectiveness.

Assumption 3: Full Clip Art Library. The final difference is that our drawer models can select from the full clip art library. Humans are only given access to a smaller set so that it can easily fit in the user interface, while ensuring that all pieces needed to reconstruct the target scene are available. We choose to adopt the full-library condition as the standard for models because it gives the models greater latitude to make mistakes (making the problem more challenging) and makes it easier to detect obviously incorrect groundings.

4.1. RULE-BASED NEAREST-NEIGHBOR METHODS:

Simple methods can be quite effective even for what appear to be challenging tasks, so we begin by building models based on nearest-neighbors and rule-based approaches.

Rule-based Nearest-Neighbor Teller. For our first Teller model, We consider a rule-based dialog policy where the Teller describes exactly one clip art each time it talks. The rule-based system determines which clip art to describe during each round of conversation, following a fixed order that roughly starts with objects in the sky (sun, clouds, airplanes), then objects in the scene (trees, Mike, Jenny), and ends with small objects (sunglasses, baseball bat). The Teller then produces an utterance by performing a nearest-neighbor lookup in a database containing (Teller utterance, clip art object) pairs, where the similarity between the selected clip art and each database element is measured by applying the scene similarity metric to individual clip art. The database is constructed by collecting all instances in the training data where the Teller sent a message and the Drawer responded by adding a single clip art piece to the canvas. Instances where the Drawer added multiple clip art pieces or made any changes to the position or other attributes of pieces already on the canvas are not used when constructing the nearest-neighbor database. This baseline approach is based on the assumptions that the Drawer's action was elicited by the Teller utterance immediately prior, and that the Teller's utterance will have a similar meaning when copied verbatim into a new conversation.

Rule-based Nearest-Neighbor Drawer. This Drawer model is the complement to the rule-based nearest-neighbor Teller. It likewise follows a hard-coded rule that the response to each Teller utterance should be the addition of a single clip art to the scene, and makes use of the same database of (Teller utterance, clip art object) tuples collected from the training data. Each Teller utterance the agent receives is compared with the stored tuples using character-level string edit distance. The clip art object from the most similar tuple is selected and added to the canvas by the Drawer.

4.2. NEURAL DRAWER:

In this section, we describe a neural network approach to the Drawer. 

Teller Drawer:

Figure 2 : A sketch of our model architectures for the neural Drawer and Teller. The Drawer (left) conditions on the current state of the canvas and a BiLSTM encoding of the previous utterance to decide which clip art pieces to add to a scene. The Teller (right) uses an LSTM language model with attention to the scene (in blue) taking place before and after the LSTM. The "thought bubbles" represent intermediate supervision using an auxiliary task of predicting which clip art have not been described yet. In reinforcement learning, the intermediate scenes produced by the drawer are used to calculate rewards. Note that the language used here was constructed for illustrative purposes, and that the messages in our dataset are more detailed and precise.

When considering past history, we make the Markovian assumption that the current state of the Drawer's canvas captures all information from the previous rounds of dialog. Thus, the Drawer need only consider the most recent utterance from the Teller and the current canvas to decide what to draw next. We experimented with incorporating additional context -such as previous messages from the teller or the action sequence by which the Drawer arrived at its current canvas configuration -but did not observe any gains in performance.

The current state of the canvas is represented using a collection of indicator features and real-valued features. For each of the n c = 58 clip art types, there is an indicator feature for its presence on the canvas, and an indicator feature for each discrete assignment of an attribute to the clip art (e.g. 1 size=small , 1 size=medium , etc.) for a total of n b = 41 binary features. There are additionally two real-valued features that encode the x and y position of the clip art on the canvas, normalized to the 0-1 range. The resulting canvas representation is a feature vector v canvas of size n c × (n b + 2), where all features for absent clip art types are set to zero.

We run a bi-directional LSTM over the Teller's most recent message and extract the final hidden states for both directions, which we concatenate to form a vector v message . The Drawer is then a feedforward neural network that takes as input v canvas and v message and produces an output vector v action . The action representation v action also consists of n c × (n b + 2) elements and can be thought of as a continuous relaxation of the mostly-discrete canvas encoding. For each clip art type, there is a realvalued score that determines whether a clip art piece of that type should be added to the canvas: a positive score indicates that it should be added as part of the action. During training, a binary crossentropy loss compares these scores with the actions taken by human drawers. v action also contains unnormalized log-probabilities for each attribute-value assignment (e.g. z size=small , z size=medium , etc. for each clip art type); when a clip art piece is added to the canvas, its attributes are assigned to their most-probable values. The log-probabilities are trained using softmax losses. Finally, v action contains two entries for each clip art type that determine the clip art's x, y position if added to the canvas; these elements are trained using an L 2 loss.

4.3. NEURAL TELLER: SCENE2SEQ:

For our neural Teller models, we adopt an architecture that we call scene2seq. This architecture is a conditional language model over the Teller's side of the conversation with special next-utterance tokens to indicate when the Teller ends its current utterance and waits for a reply from the Drawer. 2The language model is implemented using an LSTM, where information about the ground-truth scene is incorporated both before and after each LSTM cell through the use of an attention mechanism. Attention occurs over individual clip art pieces: each clip art object in the ground-truth scene is represented using a vector that is the sum of learned embeddings for different clip art attributes (e.g. e type=Mike , e size=small , etc.) At test time, the Teller's messages are constructed by decoding from the language model using greedy word selection.

To communicate effectively, the Teller must keep track of which parts of the scene it has and has not described, and also generate language that is likely to accomplish the task objective when interpreted by the Drawer. We found that training the scene2seq model using a maximum likelihood objective did not result in long-term coherent dialogs for novel scenes. Rather than introducing a new architecture to address these deficiencies, we explore reducing them by using alternative training objectives. To better ensure that the model keeps track of which pieces of information it has already communicated, we take advantage of the availability of drawings at each round of the recorded human dialogs and introduce an auxiliary loss based on predicting these drawings. To select language that is more likely to lead to successful task completion, we further fine-tune our Teller models to directly optimize the end-task goal using reinforcement learning.

4.3.1. TRAINING WITH INTERMEDIATE SUPERVISION:

We incorporate state tracking into the scene2seq architecture through the use of an auxiliary loss. This formulation maintains the end-to-end training procedure and keeps test-time decoding exactly the same. The only change is that, at each utterance separator token, the output from the LSTM is used to predict which clip art still need to be described. More precisely, the network must classify whether each clip art in the ground truth has been drawn already or not. The supervisory signal makes use of the fact that the CoDraw dataset records human drawer actions at each round of the conversation, not just at the end. The network outputs a score for each clip art ID, which is connected to a softmax loss for the clip art in the ground truth scene (the scores for absent clip arts do not contribute to the auxiliary loss). We find that adding such a supervisory signal reduces the Teller's propensity for repeating itself or omitting objects.

4.3.2. FINE-TUNING WITH REINFORCEMENT LEARNING:

The auxiliary loss helps the agent be more coherent throughout the dialog, but it is still trained to imitate human behavior rather than to complete the downstream task. By training the agents using reinforcement learning (RL), they can learn to use language that is more effective at achieving highfidelity scene reconstructions. In this work we only train the Teller with RL, because the Teller has challenges maintaining a long-term strategy throughout a long dialog, whereas preliminary results showed that making local decisions is less detrimental for Drawers.

The scene2seq Teller architecture remains unchanged, and each action from the agent is to output a word or one of two special tokens: a next-utterance token and a stop token. After each next-utterance token, our neural Drawer model is used to take an action in the scene and the resulting change in scene similarity metric is used as a reward. However, this reward scheme alone has an issue: once all objects in the scene are described, any further messages will not result in a change in the scene and have a reward of zero. As a result, there is no incentive to end the conversation. We address this by applying a penalty of 0.3 to the reward whenever the Drawer makes no changes to the scene. We train our model with REINFORCE (Williams, 1992).

5. TRAINING PROTOCOL AND EVALUATION:

To evaluate our models, we pair our models with other models, as well as with a human.

Human-Machine Pairs. We modified the interface used for data collection to allow human-machine pairs to complete the tasks. Each model plays one game with a human per scene in the test set, and we compare the scene reconstruction quality between different models and with human-human pairs. Script-based Drawer Evaluation. In addition to human evaluation, we would like to have automated evaluation protocols that can quickly estimate the quality of different models. Drawer models can be evaluated by pairing them with a Teller that replays recorded human conversation from a script (a recorded dialog from the dataset) and measuring scene similarity at the end of the dia- Machine-Machine Evaluation. Unlike Drawer models, Teller models cannot be evaluated using a "script" from the dataset. We instead consider an evaluation where a Teller model and Drawer model are paired, and their joint performance is evaluated using the scene similarity metric.

5.1. CROSSTALK TRAINING PROTOCOL:

Automatically evaluating agents, especially in the machine-machine paired setting, requires some care because a pair of agents can achieve a perfect score while communicating in a shared code that bears no resemblance to natural language. There are several ways such co-adaptation can develop. One is by overfitting to the training data to the extent that it's used as a codebook -we see this with the rule-based nearest-neighbor agents described in Section 4.1, where a Drawer-Teller pair "trained" on the same data outperforms humans on the CoDraw task. An examination of the language, however, reveals that only limited generalization has taken place (see Figure 3 ). Another way that agents can co-adapt is if they are trained jointly, for example using reinforcement learning.

To limit these sources of co-adaptation, we propose a training protocol we call "crosstalk." In this setting, the training data is split in half, and the Teller and Drawer are trained separately on disjoint halves of the training data. When multiple agents are required during training (as with reinforcement learning), the joint training process is run separately for both halves of the training data, but evaluation pairs a Teller from the first partition with a Drawer from the second. This ensures to some extent that the models can succeed only if they have learned generalizable language, and not via a highly specialized codebook specific to model instances. 

6. RESULTS:

Results for our models are shown in Table 1 . All numbers are scene similarities, averaged across scenes in the test set.

Neural Drawer Performs the Best. In the script setting, our neural Drawer is able to outperform the rule-based nearest-neighbor baseline (3.39 vs. 0.94) and close most of the gap between baseline (0.94) and human performance (4.17).

Validity of Script-Based Drawer Evaluation. To test the validity of script-based Drawer evaluation -where a Drawer is paired with a Teller that recites the human script from the dataset corresponding to the test scenes -we include results from interactively pairing human Drawers with a Teller that recites the scripted messages. While average scene similarity is lower than when using live human Tellers (3.83 vs. 4.17), the scripts are sufficient to achieve over 91% of the effectiveness of the same Teller utterances when they were communicated live (according to our metric). The drop in similarity may be in part due to the inability of the Teller to peek at the Drawer's canvas and suggest specific corrections, and in part because the Teller can't answer clarifying questions specific to the Drawer's personal understanding of the instructions. Note that a human Drawer with a script-based Teller still outperforms our best Drawer model paired with a script-based Teller.

Benefits of Intermediate Supervision and Goal-Driven Training. Pairing our models with humans shows that the scene2seq Teller model trained with imitation learning is worse than the rulebased nearest-neighbor baseline (2.69 vs. 3.21), but that the addition of an auxiliary loss followed by fine-tuning with reinforcement learning allow it to outperform the baseline (3.65 vs. 3.21). However, there is still a gap between to human Tellers (3.65 vs. 4.17). Many participants in our human study noted that they received unclear instructions from the models they were paired with, or expressed frustration that their partners could not answer clarifying questions as a way of resolving such situations. Recall that our Teller models currently ignore any utterances from the Drawer.

Correlation Between Fully-automated and Human-machine Evaluation. We also report the result of paired evaluation for different Teller models and our best Drawer, showing that the relative rankings of the different Teller types match those we see when models are paired with humans. This shows that automated evaluation while following the crosstalk training protocol is a suitable automated proxy for human-evaluation.

6.1. TYPICAL ERRORS:

The errors made by Teller reflect two key challenges posed by the CoDraw task: reasoning about the context of the conversation and drawing, and planning ahead to fully and effectively communicate the information required. A common mistake the rule-based nearest-neighbor Teller makes is to reference objects that are not present in the current scene. Figure 3 shows an example (second panel from the right) where the Teller has copied a message referencing a "swing" that does not exist in the current scene. In a sample of 5 scenes from the test set, the rule-based nearest-neighbor describes a non-existent object 11 times, compared to just 1 time for the scene2seq Teller trained with imitation learning. The scene2seq Teller, on the other hand, frequently describes clip art pieces multiple times or forgets to mention some of them: in the same sample of scenes, it re-describes an object 10 times (vs. 2 for the baseline) and fails to mention 11 objects (vs. 2.) The addition of an auxiliary loss and RL fine-tuning reduces these classes of errors while avoiding frequent descriptions of irrelevant objects (0 references to non-existent objects, 3 instances of re-describing an object, and 4 objects omitted.)

On the Drawer side, the most salient class of mistakes made by the neural network model is semantically inconsistent placement of multiple clip art pieces. Several instances of this can be seen in 

7. CONCLUSION:

In this paper, we introduce CoDraw: a collaborative task designed to facilitate learning of effective natural language communication in a grounded context. The task combines language, perception, and actions while permitting automated goal-driven evaluation both at the end and as a measure of intermediate progress. We introduce a dataset and models for this task, and propose a crosstalk training + evaluation protocol that is more generally applicable to studying emergent communication. The models we present in this paper show levels of task performance that are still far from what humans can achieve. Long-term planning and contextual reasoning as two key challenges for this task that our models only begin to address. We hope that the grounded, goal-driven communication setting that CoDraw is a testbed for can lead to future progress in building agents that can speak more naturally and better maintain coherency over a long dialog, while being grounded in perception and actions.

A.2 PARTICIPANT STATISTICS:

We found that approximately 13.6% of human participants disconnect voluntarily in an early stage of the session. We paid participants who stayed in the conversation and had posted at least three messages. However, we exclude those incomplete sessions in the dataset, and only use the completed sessions.

There are 616 unique participants represented in our collected data. Among these workers, the 5 most active have done 26.63% of all finished tasks (1,419, 1,358, 1,112, 1,110, and 1,068 tasks) . Across all workers, the maximum, median, and minimum numbers of tasks finished by are 1,419, 3, and 1, respectively. 

B DATASET STATISTICS:

The CoDraw dataset consists of 9,993 dialogs consisting of a total of 138K utterances. Each dialog describes a distinct abstract scene.

Messages. Figure 6a shows the distribution of message lengths for both Drawers and Tellers. Drawer messages tend to be short (the median length is 1 accounts for 62% of messages), but there does exist a heavy tail where the Drawer asks clarifying questions about the scene. Teller message length have a more smooth distribution with a median length of 16 tokens. The size of vocabulary is 4,555: since conversations describe abstract scenes consisting of a limited number of clip art types, the vocabulary is relatively small compared to tasks involving real images.

Rounds. Figure 6b shows the distribution of the numbers of conversational rounds for dialog sessions. Most interactions are shorter than 20 rounds, median being 7.

Durations. In Figure 6c we see that the median session duration is 6 minutes. We had placed a 20-minute maximum limit on each session.

Scores. Figure 7 shows the distribution of scene similarity scores throughout the dataset. Figure 8 shows the progress of scene similarity scores over the rounds of a conversation. An average conversations is done improving the scene similarity after about 5 rounds, but for longer conversations that continue to 23 rounds, there is still room for improvement.

Figure 7 : The distribution of overall scores (section 3.3) at the end of the dialog. 

s(C, Ĉ) = c∈C∩ Ĉ f (c) |C ∪ Ĉ| + ci,cj ∈C∩ Ĉ,i<j g(c i , c j ) |C ∪ Ĉ|(|C ∩ Ĉ| -1)

where 

f (c) =w 0 -w 1 1 clip art piece c

-w 5 xc -x c W 2 + ŷc -y c H 2 g(c i , c j ) = -w 6 1 (xc i -xc j )(xc i -xc j )<0 -w 7 1 (ŷc i -ŷc j )(yc i -yc j )<0

Here x c and y c refer to the position of the clip art piece in the ground-truth scene, xc and ŷc refer to its position in the predicted scene, and W, H are the width and height of the canvas, respectively.

We use parameters w = [5, 1, 0.5, 0.5, 1, 1, 1, 1], which provides a balance between the different components and ensures that scene similarities are constrained to be between 0 and 5.

D QUALITATIVE EXAMPLES:

Figure 9 shows some examples of scenes and dialogs from the CoDraw dataset. The behavior of our Drawer and Teller models on a few randomly-selected scenes is illustrated in Figures 10, 11 

:

Excluding

empty scenes from the original dataset.

Though none of the models in this paper handle language in the Drawer replies, these can be incorporated into the scene2seq framework similar to the approach ofLewis et al. (2017).

Figures and Tables:

Figure 1: Overview of the proposed Collaborative Drawing (CoDraw) task. The game consists of two players -Teller and Drawer. The Teller sees an abstract scene, while the Drawer sees an empty canvas. Both players need to collaborate and communicate so that the Drawer can reconstruct the image of the Teller by dragging and dropping clip art objects.

Figure 3: A rule-based nearest-neighbor Teller/Drawer pair "trained" on the same data outperforms humans for this scene according to the similarity metric, but the language used by the models doesn't always correspond in meaning to the actions taken. The three panels on the left show a scene from the test set and corresponding human/model reconstructions. The two panels on the right show the Teller message and Drawer action from two rounds of conversation by the machine agents.

Figure 10 in the Appendix D, where the Drawer places a hat in the air instead of on a person's head, or where the drawn clip art pieces overlap in a visually unnatural way. Qualitative examples of both human and model behavior are provided in Appendix D.

Figure 8: Average scene similarity plotted for different conversation rounds. On the left, only conversations that have reached the given number of rounds are included. On the right, conversations that end early are padded to 35 rounds through the addition of empty messages/actions.

faces the wrong direction -w 2 1 clip art piece c is Mike or Jenny and has the wrong facial expression -w 3 1 clip art piece c is Mike or Jenny and has the wrong body pose -w 4 1 clip art piece c has the wrong size

Figure9shows some examples of scenes and dialogs from the CoDraw dataset. The behavior of our Drawer and Teller models on a few randomly-selected scenes is illustrated inFigures 10, 11, and 12.

We collect a CoDraw dataset of ∼10K variable-length dialogs consisting of ∼138K messages with the drawing history at each step of the dialog.• We define a scene similarity metric, which allows us to automatically evaluate the effectiveness of the communication at the end and at intermediate states.• We propose a cross-talk training and evaluation protocol that prevents agents from potentially learning joint uninterpretable codebooks, rendering them ineffective at communicating with humans. • We evaluate several Drawer and Teller models automatically as well as by pairing them with humans, and show that long-term planning and context reasoning in the conversation are key challenges of the CoDraw task.

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is tilted down to right not straight on head . and i am going to peek also

Table content: <table><tr><td>Ground Truth Scene</td><td>Human Reconstruction</td><td>Machine Reconstruction</td></tr><tr><td/><td>similarity: 4.32</td><td>similarity: 4.85</td></tr></table>



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Results for our models on the test set, using three types of evaluation: script-based (i.e. replaying Teller utterances from the dataset), human-machine, and machine-machine pair evaluation.

Table content: <table><tr><td/><td/><td>Teller</td><td>Drawer</td><td>Scene similarity</td></tr><tr><td>Script-based</td><td/><td colspan="2">Script (replays human-generated messages) Rule-based Nearest Neighbor Script (replays human-generated messages) Neural Network Script (replays human-generated messages) Human</td><td>0.94 3.39 3.83</td></tr><tr><td>Human-Machine</td><td>    </td><td>Rule-based Nearest Neighbor Scene2seq (imitation learning) + auxiliary loss + RL fine-tuning</td><td>Human Human Human Human</td><td>3.21 2.69 3.04 3.65</td></tr><tr><td>Machine-Machine</td><td>    </td><td>Rule-based Nearest Neighbor Scene2seq (imitation learning) + auxiliary loss + RL fine-tuning</td><td>Neural Network Neural Network Neural Network Neural Network</td><td>3.08 2.67 3.02 3.67</td></tr><tr><td/><td/><td>Human</td><td>Human</td><td>4.17</td></tr></table>

Figure 6: Statistics of the CoDraw dataset. (a) The distribution of the number of tokens in Teller's (blue) and Drawer's (green) messages. Notice that the number of single-token messages of Drawer is 41,195 (62.06%). The medians for Teller and Drawer are 16 and 1, respectively. (b) The distribution of the numbers of conversational rounds. The median is 7 rounds. (c) The distribution of the duration of dialog sessions. The median is 6 minutes.

Table content: <table><tr><td colspan="4">*Collected 9,993 sessions as of Apr 19 2017</td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td>The number of messages</td><td>0K 2K 4K 6K 8K 10K</td><td>Median 1 41,195</td><td>Drawer Median 16</td><td>Teller</td><td>The number of sessions</td><td>.0K .4K .8K 1.2K 1.6K 2.0K</td><td>Median 7</td><td>The number of sessions</td><td>.0K .4K .8K 1.2K 2.0K 1.6K</td><td>Median 6</td></tr><tr><td/><td/><td colspan="3">1 4 7 10 13 16 19 22 25 28 31 34 37</td><td/><td/><td>1 4 7 10 13 16 19 22 25 28 31 34</td><td/><td/><td>1 3 5 7 9 11 13 15 17 19</td></tr><tr><td>a.</td><td/><td/><td>The number of tokens</td><td/><td>b.</td><td/><td>The number of rounds</td><td>c.</td><td/><td>Duration (minutes)</td></tr><tr><td>The number of sessions</td><td>.0K .4K .8K 1.2K 2.0K 1.6K</td><td/><td colspan="2">Median 4.2</td><td/><td/><td/><td/><td/><td/></tr><tr><td/><td/><td colspan="3">0.0 0.4 0.8 1.2 1.6 2.0 2.4 2.8 3.2 3.6 4.0 4.4 4.8</td><td/><td/><td/><td/><td/><td/></tr><tr><td/><td/><td colspan="3">Scene Similarity Metric (score)</td><td/><td/><td/><td/><td/><td/></tr></table>

Peer review (iclr2019):
 This paper presents CoDraw, a grounded and goal-driven dialogue environment for collaborative drawing. The authors argue convincingly that an interactive and grounded evaluation environment helps us better measure how well NLG/NLU agents actually understand and use their language — rather than evaluating against arbitrary ground-truth examples of what humans say, we can evaluate the objective end-to-end performance of a system in a well-specified nonlinguistic task. They collect a novel dataset in this grounded and goal-driven communication paradigm, define a success metric for the collaborative drawing task, and present models for maximizing that metric. This is a very interesting task and the dataset/models are a very useful contribution to the community. I have just a few comments below: 1. Results: 1a. I’m not sure how impressed I should be by these results. The human–human similarity score is pretty far above those of the best models, even though MTurkers are not optimized (and likely not as motivated as an NN) to solve this task. You might be able to convince me more if you had a stronger baseline — e.g. a bag-of-words Drawer model which works off of the average of the word embeddings in a scripted Teller input. Have you tried baselines like these? 1b. Please provide variance measures on your results (within model configuration, across scene examples). Are the machine–machine pairs consistently performing well together? Are the humans? Depending on those variance numbers you might also consider doing a statistical test to argue that the auxiliary loss function and and RL fine-tuning offer certain improvement over the Scene2seq base model. 2. Framing: there is a lot of work in collaborative / multi-agent dialogue models which you have missed — see refs below to start. You should link to this literature (mostly in NLP) and contrast your task/model with theirs. References Vogel & Jurafsky (2010). Learning to follow navigational directions. He et al. (2017). Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings. Fried et al. (2018). Unified pragmatic models for generating and following instructions. Fried et al. (2018). Speaker-follower models for vision-and-language navigation. Lazaridou et al. (2016). The red one!: On learning to refer to things based on their discriminative properties.