Do Encoder Representations of Generative Dialogue Models have sufficient summary of the Information about the task ? (2021)

Abstract: Predicting the next utterance in dialogue is contingent on encoding of users’ input text to generate appropriate and relevant response in data-driven approaches. Although the semantic and syntactic quality of the language generated is evaluated, more often than not, the encoded representation of input is not evaluated. As the representation of the encoder is essential for predicting the appropriate response, evaluation of encoder representation is a challenging yet important problem. In this work, we showcase evaluating the text generated through human or automatic metrics is not sufficient to appropriately evaluate soundness of the language understanding of dialogue models and, to that end, propose a set of probe tasks to evaluate encoder representation of different language encoders commonly used in dialogue models. From experiments, we observe that some of the probe tasks are easier and some are harder for even sophisticated model architectures to learn. And, through experiments we observe that RNN based architectures have lower performance on automatic metrics on text generation than transformer model but perform better than the transformer model on the probe tasks indicating that RNNs might preserve task information better than the Transformers.

## introduction the task of dialogue modeling requires learning through interaction, often, from humans. the model is expected to understand the input text for it to interact, and the interaction can be meaningful only when the language understanding gets better. approaches for solving dialogue task include information retrieval based approaches like selecting a response from a set of canned responses @xcite or keeping track of very specific information which are a priori marked as informative slot-value pairs @xcite or generating the next response with token-by-token @xcite @xcite @xcite . the evaluation of the different approaches have mostly relied on the output of the model -the slot predicted, response selected or generated. the issues in evaluation -automatic evaluation metrics uncorrelated with human judgement -showcased by @xcite is still an open problem. attempts to mimic human scores for better evaluation metric @xcite and other metrics that aim to correlate with the human judgement @xcite evaluate the quality of the text generated but do not evaluate the language understanding component of a model. the language understanding component of an agent more often than not goes unnoticed with only token-level evaluation metrics on the generated text. to that end, we propose evaluating the encoder representation of dialogue models through probe tasks 1 constructed from the commonly used dialogue data sets -multiwoz @xcite and personachat @xcite . concretely, we use the representation learnt by the encoders while training on dialogue generation tasks to solve a set of dialogue related classification tasks as a proxy to probe the information encoded in the encoder representation. we study the performance of language encoders in 17 different probe tasks with varying degree of difficulties -binary classification, multi-label classification and multi-label prediction. for example, predicting whether the current dialogue has single or multiple tasks, identifying the number of tasks, identifying the tasks, presence of a specific information provided by the user among many others. the probe tasks allow a way to quantify the understanding of a model and help identify biases, if any, in the task of dialogue prediction. we observed the performance of the models in the probe tasks to little fluctuate with different seed values thus allowing to analyse the encoder representation with minimal variance. further, the experiments on probe tasks help in understanding deeper differences in between recurrent neural network (rnn) and transformer encoders that were previously not evident from the token-level evaluation methods. our contributions in the paper are: ## related work evaluating dialogue models has been an important topic of study. while many of the metrics have focussed on evaluating the generated text through n-gram overlap based heuristics -bleu @xcite , rouge @xcite , meteor (lavie and agarwal, 2007) -there have also been learned metrics like adem @xcite , maude @xcite , ruber @xcite among other metrics @xcite . though language generation has been an important component of study, there are not many studies that benchmark soundness of encoding information by dialogue systems. probe tasks in language generation @xcite @xcite has been used to understand the information encoded in continuous embedding of sentences. such probe tasks are set up as classification tasks that are solved with model learnt representation. as it is easier to control the biases in probe tasks than in the downstream tasks, research in language generation has analysed models on probe tasks like using encoder representation to identify words in input (wordcont) to measuring encoder sensitivity to shifts in bigrams @xcite . analysis using probe tasks has been done also in reinforcement learning (rl). @xcite learn state representation for an rl agent in an unsupervised setting and introduce a set of probe tasks to evaluate the representation learnt by agents. this includes using an annotated data set with markers for position of the agent, current score, items in inventory, target's location among others. the authors train a shallow linear classifier to identify specific entities in the embedded input that serves as a metric for the representational soundness of the learning algorithm. applications of computer vision like caption generation for images @xcite or videos @xcite use attention based models to parse over the hidden states of a convolutional neural network (convnet) @xcite . the attention over the convnet features are visualized to observe the words corresponding to different parts of the image. visualizing the attention has been one of the qualitative probe task for text generation conditioned on images @xcite . ## discussion it is also interesting to draw parallels to unit testing in software engineering @xcite , where the smallest software components of a system are tested for their design and logical accuracy. the difference between a deterministic application software and a stochastic decision making ml module is that the behavior of the ml system is data-driven while for a software system it is driven by logic. despite the difference, the unit testing and probe tasks could share a common ground towards ensuring the better representation of the encoded contexts. dialogue models as an alternate to tokenlevel evaluation, comparison of different model architectures can be meaningfully made with an aggregate metric on the probe tasks in three groups of difficulty -easy ((ave. seq2seq) untrained @xmath0.50), medium(0.25 < untrained @xmath1.50), and hard (untrained @xmath2.25). such an analysis, as shown in ## conclusion we propose a set of probe tasks to evaluate the encoder representation of end-to-end generative dialogue models. we observed that mimicking surface level token prediction do not reveal much about a model's ability to understand a natural language context. the results on probe tasks showed that rnn-based models perform better than transformer model in encoding information in the context. we also found some probe tasks that all of the models find difficult to solve; this invites novel architectures that can handle the language understanding aspects in dialogue generation. although language generation is required for a dialogue model, the performance in token/response prediction alone cannot be a proxy for the model's ability to understand a conversation. hence, systematically identifying issues in language understanding through probe tasks can help in building better models and collecting challenging data sets.