Effect of Pressure for Compositionality on Language Emergence

1 INTRODUCTION . Traditional language processing has been an enormous success and employs large amounts of textual examples to build statistical relationships . However , language grounding and capturing of functional aspects achieved by such methods are questionable ( Ren et al. , 2020 ; Lazaridou et al. , 2017 ; Mordatch & Abbeel , 2018 ; Lazaridou et al. , 2018 ) . Language emergence is an alternative to supervised approaches , where neural agents develop communications by themselves without being exposed to explicit supervising examples . Agents are deployed in a partially observable environment , such that specific agents are unable to complete their assigned tasks without gaining information from their peers ( Jorge et al. , 2016 ) . Agents should develop their own language to acquire information on what they can not observe . Hence , the emerged communications , which are born out of necessity , tend to be more pragmatic . Agents map environmental entities or concepts with symbols while reaching a consensus for the mapping . If agents follow compositionality , agents would connect individual symbols to atomic concepts in the environment , and combine multiple symbols to form composite messages for expressing complex novel concepts , facilitating generalization . For example , Red Square is a compositional expression , containing attributes Color and Shape , constructed by using atomic concepts Red and Square , which corresponds to the values taken by the two attributes . Hence , whenever an agent observes a novel input , it can break down the input into a set of attributes and corresponding values . An expression describing the input can be constructed by suitably concatenating the symbols representing each value of the attributes . It is still in debate whether compositionality is an essential or a desired property in language emergence . The simple reason for favoring compositionality is that it enables expressing complex concepts using simpler ones , implying a higher degree of generalization for unseen data . Moreover , there is strong evidence ( Kirby & Hurford , 2002 ; Kirby et al. , 2014 ; 2015 ) citing this property as advantageous for language acquisition . Such ideas and proposals have led several studies to promote compositionality as a required attribute in language emergence ( Ren et al. , 2020 ; Lazaridou et al. , 2018 ) . Nevertheless , some others ( Chaabouni et al. , 2020 ) have shown empirical evidence that , for the case of language emergence in neural agents , compositionality is not related to generalization . ( Chaabouni et al. , 2020 ; Gupta et al. , 2020 ; Resnick et al. , 2020 ) have studied the relationship between compositionality and several other parameters like generalization , bandwidth , and agent complexity . ( Ren et al. , 2020 ) propose a model based on the generational transmission that favors compositional languages . They demonstrate the increased learning speed of compositional languages and propose the existence of a strong correlation between compositionality and validation performance . Previous works discuss the usage of functional pressures in neural language emergence . ( Chaabouni et al. , 2019 ) found that agents develop anti-efficient encoding in emergent communications without complying with the principle of least effort as described by the Zipf law ( Zipf , 2016 ) . Message distributions begin to follow the Zipf law when the cost function includes a penalty for longer messages . ( Choi et al. , 2018 ) study compositional obverter technique , stressing that what speaker agents are transmitting should also be understandable to themselves . Obverter technique observes Zipf ’ s law , making communications more efficient , as stated by the latter study . ( Kirby & Hurford , 2002 ) imposes a similar constraint for following the principle of least effort by selecting the shortest from a set of generated strings . Being motivated by such studies , we incorporate an auxiliary loss based on topological similarity into the training process , which pressures agents to make their messages compositional . It is different from ( Chaabouni et al. , 2020 ) where there is no such external functional pressure . ( Kirby & Hurford , 2002 ; Kirby et al. , 2014 ; 2015 ) discuss cultural evolution of language and propose compositionality as a quality stemming from iterated learning , where successive generations acquire language skills from the previous generations . ( Ren et al. , 2020 ) uses an iterated learning approach yielding languages with higher generalizations and learning speed advantages . Their method has multiple phases for learning , interacting , and transmission , which is resource-consuming . We do not employ generational transmission or additional learning phases other than the main reconstruction game . Our agents acquire compositionality within the learning phase of a single life cycle . Hence , the proposed approach is simple to implement and significantly time efficient . We conduct experiments using inputs with varying degrees of complexity , and agents built with different neural architectures , to study agents describing a given input over a discrete communication channel . We externally constrain the structure of messages that agents can send through the channel . We measure agent performance during testing as an indicator for generalization . First , we study the relationship between compositionality and generalization subjected to agent architecture , message structure , and input complexity without providing any external influence . Then , we train agents with our proposed auxiliary loss , pressuring neural agents to various extents to adhere to compositionality in emerging communication protocols . We expect that agents trained under suitable compositional pressures will exhibit improvements over the baseline during the testing . Our results show that improving generalization and convergence speed is possible by carefully adjusting the compositional pressure . The optimal pressure depends on the message structure , input space , and agent architecture . Furthermore , training under high compositional pressures emerge languages that are substantially easier to learn by new learners . We observed a situational correlation between compositionality and generalization even when agents learn under no external pressure . In general , well-tuned external pressure for compositionality gives improved results on average . We summarize the contributions of this paper as follows , • We propose a straightforward method to externally induce compositionality , which can increase generalization , convergence and transmission speed in language emergence . • We find that , there is a situational correlation , which can be considerably strong depending on the environment parameters , between compositionality and generalization , even without external pressure . 2 LANGUAGE EMERGENCE GAME . There are several widely used environment configurations in use , most of which are inspired by the Lewis signaling game ( Lewis , 2008 ) . ( Sukhbaatar et al. , 2016 ; Mordatch & Abbeel , 2018 ) used a simulating environment where multiple agents can navigate in a 2-D world while coordinating with each other by exchanging discrete symbols . ( Lazaridou et al. , 2017 ; Havrylov & Titov , 2017 ) . ( Havrylov & Titov , 2017 ; Evtimova et al. , 2018 ; Jorge et al. , 2016 ) employ a ” discriminating ” objective where one of the agents has to correctly identify a reference object apart from a set of distractors by listening to its peer who has a copy of the reference . ( Kharitonov et al. , 2020 ) propose ” classification ” type game . In such variations speaker , who gets an image , transmits discrete signals to the receiver . The receiver then determines the class of the image referenced by the Speaker . ( Resnick et al. , 2020 ; Gupta et al. , 2020 ) follows the ” reconstruction ” game setup , where Listener has to approximate the input given to the Speaker . Discriminating , classification , and reconstruction games frequently but not always use a value attribute environment . In such environments , inputs consist of a set of abstract attributes , and each attribute can take a finite set of values . The whole dataset is a collection of abstract vectors , which are free from noise . Such data is easier to generate , and one can easily control the dataset complexity by changing the number of attributes and values . ( Kharitonov et al. , 2020 ; Bouchacourt & Baroni , 2018 ) discuss how agents can converge to a simpler protocol without capturing high-level features in a discrimination game . For example , if images are used , agents can use the average pixel intensities of the images to identify the correct image . In a value-attribute environment , such behaviors cause agents to have an excessive degree of freedom . Achieving high performance is still possible if the complexity of the distractor set is low . Hence , the final accuracy may not directly indicate a rich communication protocol or a vocabulary . Therefore , in our work , we use the reconstruction objective ( Resnick et al. , 2020 ; Gupta et al. , 2020 ) to remove the possibility of such events . 2.1 SETUP . Our game consist of two agents Speaker and Listener . First Speaker observes an object and transmits a single discrete message to the Listener . After reading the entire message , Listener tries to predict the object seen by the Speaker . If Listeners prediction is correct both agents are rewarded ( see Figure 1 ) . Our experiments use abstract objects from the commonly used value attribute environment ( Resnick et al. , 2020 ; Gupta et al. , 2020 ; Ren et al. , 2020 ) . Each object is described by a set of attributes , and each attribute can take a single value from a set of values . We create an abstract object x ∈ X composed by a set of attributes a ∈ A where each attribute takes a value v ∈ V. We externally control the number of attributes |A| = NA and the number of values per attribute |V| = NV . Hence , the total objects in X is |X| = NV NA . For a given object , we iterate through all of its attributes and obtain the value of each attribute . Next , each value is converted to a one-hot encoded vector of size NV . Then all converted vectors are concatenated together to obtain the final representation of the object . For simplicity , we keep the number of values for each attribute the same . Hence , to create an object , we sample NA random integers from a range of 0− ( NV − 1 ) and concatenate the one-hot encoded representations of the sampled integers . ( Chaabouni et al. , 2020 ; Ren et al. , 2020 ; Resnick et al. , 2020 ) use environments with attributes of similar values , where the instances from different studies are identical if the number of attributes and values are the same . Nonetheless , agents ’ internal representations can be different depending on the occasion . In the game , Speaker , after observing the input x , transmit a discrete message m ∈ M towards a Listener . After reading the message , Listener reconstructs an approximation x′ for the original object . To construct the message , Sender repeatedly samples symbols or words w ∈ W from a finite-sized vocabulary with replacement until the message reaches its maximum allowed length L. Sampled symbols are concatenated together in order to construct the message m. Similar to inputs , each symbol is represented as a one-hot encoded vector . Then the whole message is transmitted to the Listener . The maximum number of unique messages Speaker can construct , or the message space capacity |M| is equal to |W|L . For convenience , we denote the input spaces and message space as tuples , where X ( V , A ) and M ( W , L ) denoting data spaces with V values , A attributes and message spaces with vocabulary size W and maximum message length L .

Summary: The paper is an incremental study that tries to go beyond some existing baseline in the generalization ability of language learned through message exchanges between two neural agents (sender and listener).  The proposed method is to add to the optimization objective of the agents the effect of compositional pressure using a metric called topological similarity, under a set-up of the game where the primary objective is reconstruction.  Senders are said to sample from some message space a certain representation about an object.  The listener tries to decode the message and reconstruct the object.  Using some synthetic experiments, it is shown that though in some cases the method has an advantage on generalization, the advantage is not consistent but depends substantially on the agent architecture, structure of input data, and parameters of the game environment.