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 authors create a language game in which one neural agent describes an item to another.  They study what happens to the language that emerges if an explicit parameter promoting compositionality is altered, considering a range of different environmental factors. The results show an impact of this parameter on the speed at which communication converges and the degree of generalisation of the language