Local Explanation of Dialogue Response Generation

1 Introduction . As we use machine learning models in daily tasks , such as medical diagnostics [ 6 , 19 ] , speech assistants [ 31 ] etc. , being able to trust the predictions being made has become increasingly important . To understand the underlying reasoning process of complex machine learning models a sub-field of explainable artificial intelligence ( XAI ) [ 2 , 17 , 36 ] called local explanations , has seen promising results [ 35 ] . Local explanation methods [ 27 , 39 ] often approximate an underlying black box model by fitting an interpretable proxy , such as a linear model or tree , around the neighborhood of individual predictions . These methods have the advantage of being model-agnostic and locally interpretable . Traditionally , off-the-shelf local explanation frameworks , such as the Shapley value in game theory [ 38 ] and the learning-based Local Interpretable Model-agnostic Explanation ( LIME ) [ 35 ] have been shown to work well on classification tasks with a small number of classes . In particular , there has been work on image classification [ 35 ] , sentiment analysis [ 8 ] , and evidence selection for question answering [ 32 ] . However , to the best of our knowledge , there has been less work studying explanations over models with sequential output and large class sizes at each time step . An attempt by [ 1 ] aims at explaining machine translation by aligning the sentences in source and target languages . Nonetheless , unlike translation , where it is possible to find almost all word alignments of the input and output sentences , many text generation tasks are not alignment-based . We further explore explanations over sequences that contain implicit and indirect relations between the input and output utterances . 1Our code is available at https : //github.com/Pascalson/LERG . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . In this paper , we study explanations over a set of representative conditional text generation models – dialogue response generation models [ 45 , 55 ] . These models typically aim to produce an engaging and informative [ 3 , 24 ] response to an input message . The open-ended sentences and multiple acceptable responses in dialogues pose two major challenges : ( 1 ) an exponentially large output space and ( 2 ) the implicit relations between the input and output texts . For example , the open-ended prompt “ How are you today ? ” could lead to multiple responses depending on the users ’ emotion , situation , social skills , expressions , etc . A simple answer such as “ Good . Thank you for asking. ” does not have an explicit alignment to words in the input prompt . Even though this alignment does not exist , it is clear that “ good ” is the key response to “ how are you ” . To find such crucial corresponding parts in a dialogue , we propose to extract explanations that can answer the question : “ Which parts of the response are influenced the most by parts of the prompt ? ” To obtain such explanations , we introduce LERG , a novel yet simple method that extracts the sorted importance scores of every input-output segment pair from a dialogue response generation model . We view this sequence prediction as the uncertainty estimation of one human response and find a linear proxy that simulates the certainty caused from one input segment to an output segment . We further derive two optimization variations of LERG . One is learning-based [ 35 ] and another is the derived optimal similar to Shapley value [ 38 ] . To theoretically verify LERG , we propose that an ideal explanation of text generation should adhere to three properties : unbiased approximation , intra-response consistency , and causal cause identification . To the best of our knowledge , our work is the first to explore explanation over dialog response generation while maintaining all three properties . To verify if the explanations are both faithful ( the explanation is fully dependent on the model being explained ) [ 2 ] and interpretable ( the explanation is understandable by humans ) [ 14 ] , we conduct comprehensive automatic evaluations and user study . We evaluate the necessity and sufficiency of the extracted explanation to the generation model by evaluating the perplexity change of removing salient input segments ( necessity ) and evaluating the perplexity of only salient segments remaining ( sufficiency ) . In our user study , we present annotators with only the most salient parts in an input and ask them to select the most appropriate response from a set of candidates . Empirically , our proposed method consistently outperforms baselines on both automatic metrics and human evaluation . Our key contributions are : • We propose a novel local explanation method for dialogue response generation ( LERG ) . • We propose a unified formulation that generalizes local explanation methods towards sequence generation and show that our method adheres to the desired properties for explaining conditional text generation . • We build a systematic framework to evaluate explanations of response generation including automatic metrics and user study . 2 Local Explanation . Local explanation methods aim to explain predictions of an arbitrary model by interpreting the neighborhood of individual predictions [ 35 ] . It can be viewed as training a proxy that adds the contributions of input features to a model ’ s predictions [ 27 ] . More formally , given an example with input features x = { xi } Mi=1 , the corresponding prediction y with probability f ( x ) = Pθ ( Y = y|x ) ( the classifier is parameterized by θ ) , we denote the contribution from each input feature xi as φi ∈ R and denote the concatenation of all contributions as φ = [ φ1 , ... , φM ] T ∈ RM . Two popular local explanation methods are the learning-based Local Interpretable Model-agnostic Explanations ( LIME ) [ 35 ] and the game theory-based Shapley value [ 38 ] . LIME interprets a complex classifier f based on locally approximating a linear classifier around a given prediction f ( x ) . The optimization of the explanation model that LIME uses adheres to : ξ ( x ) = argmin ϕ [ L ( f , ϕ , πx ) + Ω ( ϕ ) ] , ( 1 ) where we sample a perturbed input x̃ from πx ( x̃ ) = exp ( −D ( x , x̃ ) 2/σ2 ) taking D ( x , x̃ ) as a distance function and σ as the width . Ω is the model complexity of the proxy ϕ . The objective of ξ ( x ) is to find the simplest ϕ that can approximate the behavior of f around x . When using a linear classifier dialog input text outputG intent ( a ) Controllable dialogue models input text positive negative ( b ) Explanation of classifier dialog input text outputG intent ( c ) Our concept Figure 1 : The motivation of local explanation for dialogue response generation . ( c ) = ( a ) + ( b ) . φ as the ϕ to minimize Ω ( ϕ ) [ 35 ] , we can formulate the objective function as : φ = argmin φ Ex̃∼πx ( Pθ ( Y = y|x̃ ) −φT z ) 2 , ( 2 ) where z ∈ { 0 , 1 } M is a simplified feature vector of x̃ by a mapping function h such that z = h ( x , x̃ ) = { � ( xi ∈ x̃ ) } Mi=1 . The optimization means to minimize the classification error in the neighborhood of x sampled from πx . Therefore , using LIME , we can find an interpretable linear model that approximates any complex classifier ’ s behavior around an example x. Shapley value takes the input features x = { xi } Mi=1 as M independent players who cooperate to achieve a benefit in a game [ 38 ] . The Shapley value computes how much each player xi contributes to the total received benefit : ϕi ( x ) = � x̃⊆x\ { xi } |x̃| ! ( |x|− |x̃|− 1 ) ! |x| ! [ Pθ ( Y = y|x̃ ∪ { xi } ) − Pθ ( Y = y|x̃ ) ] . ( 3 ) To reduce the computational cost , instead of computing all combinations , we can find surrogates φi proportional to ϕi and rewrite the above equation as an expectation over x sampled from P ( x̃ ) : φi = |x| |x|− 1ϕi = Ex̃∼P ( x̃ ) [ Pθ ( Y = y|x̃ ∪ { xi } ) − Pθ ( Y = y|x̃ ) ] , ∀i , ( 4 ) where P ( x̃ ) = 1 ( |x|−1 ) ( |x|−1|x̃| ) is the perturb function.2 We can also transform the above formulation into argmin : φi = argmin φi Ex̃∼P ( x̃ ) ( [ Pθ ( Y = y|x̃ ∪ { xi } ) − Pθ ( Y = y|x̃ ) ] − φi ) 2 . ( 5 ) 3 Local Explanation for Dialogue Response Generation . We aim to explain a model ’ s response prediction to a dialogue history one at a time and call it the local explanation of dialogue response generation . We focus on the local explanation for a more fine-grained understanding of the model ’ s behavior . 3.1 Task Definition As depicted in Figure 1 , we draw inspiration from the notions of controllable dialogue generation models ( Figure 1a ) and local explanation in sentiment analysis ( Figure 1b ) . The first one uses a concept in predefined classes as the relation between input text and the response ; the latter finds the features that correspond to positive or negative sentiment . We propose to find parts within the input and output texts that are related by an underlying intent ( Figure 1c ) . We first define the notations for dialogue response generation , which aims to predict a response y = y1y2 ... yN given an input message x = x1x2 ... xM . xi is the i-th token in sentence x with length M and yj is the j-th token in sentence y with length N . To solve this task , a typical sequence-to-sequence model f parameterized by θ produces a sequence of probability masses < Pθ ( y1|x ) , Pθ ( y2|x , y1 ) , ... , Pθ ( yN |x , y < N ) > [ 45 ] . The probability of y given x can then be computed as the product of the sequence Pθ ( y|x ) = Pθ ( y1|x ) Pθ ( y2|x , y1 ) ... Pθ ( yN |x , y < N ) . 2� x̃⊆x\ { xi } P ( x̃ ) = 1 ( |x|−1 ) � x̃⊆x\ { xi } 1/ �|x|−1 |x̃| � = 1 ( |x|−1 ) � |x̃| �|x|−1 |x̃| � / �|x|−1 |x̃| � = ( |x|−1 ) ( |x|−1 ) = 1 . This affirms that the P ( x̃ ) is a valid probability mass function . To explain the prediction , we then define a new explanation model Φ ∈ RM×N where each column Φj ∈ RM linearly approximates single sequential prediction at the j-th time step in text generation . To learn the optimal Φ , we sample perturbed inputs x̃ from a distribution centered on the original inputs x through a probability density function x̃ = π ( x ) . Finally , we optimize Φ by ensuring u ( ΦTj z ) ≈ g ( x̃ ) whenever z is a simplified embedding of x̃ by a mapping function z = h ( x , x̃ ) , where we define g as the gain function of the target generative model f , u as a transform function of Φ and z and L as the loss function . Note that z can be a vector or a matrix and g ( · ) , u ( · ) can return a scalar or a vector depending on the used method . Therefore , we unify the local explanations ( LIME and Shapley value ) under dialogue response generation as : Definition 1 : A Unified Formulation of Local Explanation for Dialogue Response Generation Φj = argmin Φj L ( g ( yj |x̃ , y < j ) , u ( ΦTj h ( x̃ ) ) ) , for j = 1 , 2 , ... , N . ( 6 ) The proofs of unification into Equation 6 can be found in Appendix A . However , direct adaptation of LIME and Shapley value to dialogue response generation fails to consider the complexity of text generation and the diversity of generated examples . We develop disciplines to alleviate these problems .

Summary: The authors propose a method for “explaining” dialogue responses from sequence generation models; that is, determining what parts of the input influence or “explain” certain elements of the output. The method, LERG, is “learned” in one of two ways to assign a score for each input/output token pair indicating the measure of relevance, and ultimately to link the intent between the two. The authors propose that their method must satisfy three properties: unbiased approximation (LERG should explain benefits of picking response given the context); consistency (LERG should explain each generation step); and cause identification (LERG should sort input features by importance). The authors measure the effectiveness of the proposed method in both automatic evaluations and user studies. The automatic evaluations measure the **necessity** and **sufficiency** of the explained segments of the input text by capturing the model perplexity difference when stripping the segments and only including the segments, respectively, essentially measuring how accurate the method’s explanations are at capturing the relevant segments of the input context. Human evaluations display the segments to humans and ask if they can predict the gold response. Both evaluations show their explanation method significantly outperform both random baselines and other methods from the literature.