He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues (2022)

Abstract: In this work, we define a new style transfer task: perspective shift, which reframes a dialouge from informal first person to a formal third person rephrasing of the text. This task requires challenging coreference resolution, emotion attribution, and interpretation of informal text. We explore several baseline approaches and discuss further directions on this task when applied to short dialogues. As a sample application, we demonstrate that applying perspective shifting to a dialogue summarization dataset (SAMSum) substantially improves the zero-shot performance of extractive news summarization models on this data. Additionally, supervised extractive models perform better when trained on perspective shifted data than on the original dialogues. We release our code publicly.

## introduction style transfer models change surface attributes of text while preserving the content. previous work on style transfer has focused on controlling the formality, authorial style, and sentiment of text @xcite . we propose a new style transfer task: perspective shift from dialogue to 3rd person conversational accounts ( §2). in this task, we seek to convert from an informal 1st person transcription of the dialogue to a 3rd person rephrasing of the conversation, where each line captures the information of a single utterance with relevant contextualizing information added. this task is challenging because it requires the interpretation of many discourse phenomena. in dialogue, speakers commonly use 1st and 2nd person pronouns and casual speech. speakers also convey their own emotions and opinions in their speech. converting a multi-party conversation to a singleperspective rephrasing requires pronoun resolution, formalization, and attribution of emotion/stance markers to individuals. while coreference resolution, stance detection, and formalization are often treated as separate tasks, the signal for these objectives is commingled in the dialogues. a pipeline approach would discard information necessary for any one task in the completion of the other two. we create a dataset for this task by annotating dialogues from the samsum corpus @xcite , a dialogue summarization corpus of synthetic text message conversations ( §3). for each conversation, annotators rephrase the utterances line-by-line into one or more sentences in 3rd person. unlike a summary, which condenses information to highlight the most important points, the goal of this transformation is to capture as much of the information from the original utterance as possible in a more standardized form. we fine-tune bart on this dataset as a supervised baseline under several different problem formulations, and we experiment with incorporating formality data into the training process ( §4). as a motivating use case, we demonstrate that extractive summarization over perspective-shifted dialogue is more fluent and has higher rouge scores than extractive summarization over the original dialogues ( §5). this trend holds for zero-shot performance of extractive summarization models trained on news corpora and for fully supervised training on modelgenerated perspective shift data. perspective shift can be a useful operation for extractive summarization when annotation time is limited; when additional data from out-of-domain is available; when the exact length and content of the summary is not known at annotation time; or when high faithfulness is important to the end task, but fluency is also a concern ( §5.3). ## task definition we define perspective shift as an utterance-level rephrasing task. given a dialogue and a single first-person singular and second-person pronouns are usually easily resolved in a conversational context-first-person singular refers to the speaker, while second-person pronouns generally refer to the other conversational parties-plural first-person pronouns can be less obvious to resolve. when a party in a conversation uses the pronoun "we," this plural may be referring to the other parties in the conversation, some but not all of the parties in the conversation, or a party not present in the conversation, e.g. in the utterance "i need to talk to my husband. we might have other plans." in our hand-annotated dataset, we resolve these pronouns wherever possible; if it is not clear what group the pronoun refers to, we resolve the pronoun as referring to "<the current @xmath0," e.g. "laura: we are busy" becomes "laura and others are busy". other entities in the text may also be difficult to resolve, such as those defined only at the beginning of the conversation, many turns prior to the current reference. syntactic rewriting is the problem of converting the syntax of the utterance to reflect 3rd rather than 1st person. this may involve re-conjugating verbs, e.g. converting "sam: i am busy" to "sam is busy." formalization and emotion attribution are related problems, as much of the emotion and stance information in the text is contained in informal phrases, unconventional punctuation, and emojis @xcite . typical formalization eliminates these markers without replacement @xcite . however, this makes formalization a highly lossy conversion, which may be undesirable for down-stream tasks. we aim to limit the information lost in the perspective shift operation by encoding the meanings of such informal language in the output. often this takes the form of an adverb (e.g. "sam angrily says") or a short descriptive sentence (e.g. "cam is amused"). this requires interpretation of the informal elements of the text. clearly, this task is far more complex than simply swapping pronouns for speaker names. we curate a dataset for the perspective shift operation. ## dataset creation the dataset is an annotated subset of the samsum @xcite dataset for dialogue summarization. samsum is a dataset of simulated text message conversations, ranging from 3 to 30 lines in length and with between 2 and 20 speakers. the dataset consists of 314 conversations from the train set, 368 conversations from the validation set, and 151 conversations from the test set foot_1 . we set aside the 151 conversations from test as a test split and use the other 682 conversations as training and validation data. annotators were instructed to convert each utterance individually to a formal 3rd person rephrasing, while preserving as much of the tone of the utterance as possible. annotators were required to insert the speaker's name in each rewritten utterance and remove all 1st-person pronouns. annotators were also asked to standardize grammar, remove questions, and add additional context (e.g. descriptive adverbs) to convey emotions previously expressed by emoticons. further information about annotator selection and pay, as well as a full copy of the annotation instructions, is available in appendix d. ## application: extractive summarization in the extractive summarization setting, phrases or sentences are taken directly from the input and composed into a summary. this is a clear failure case for dialogue, where sentences in the input are in first person and often pose questions or corrections to previous utterances; knowledge of other speakers in the dialogue can be necessary to contextualize the information. summaries should present an overview of a conversation that incorporates global contextual information; generally, these summaries are also expected to be in third person. extraction over a perspective-shifted dialogue does not suffer from many of the same problems as extraction over an original dialogue. the text in a perspective shifted dialogue is in formal third person, which matches the desired style of the summary text. while individual sentences of the perspective shifted dialogue correspond directly to individual utterances in the dialogue, the coreference resolution involved in the perspective shift step means that these sentences are less interdependent than the dialogue turns. in many respects, perspective shift should make the task of dialogue summarization easier. ## discussion another possible application of perspective shift for summarization is in query-specific summarization, where there is not a single canonical summary at training time. instead, a relevant span is selected and summarized based on a user query. query-specific summarization has been applied to dialogue-based domains, such as meeting summarization @xcite . in these domains, we conjecture perspective shift may make the choice of an extractive summarization model feasible, allowing for greater interpretability and faithfulness of outputs. perspective shift also appears to be a less effortful task for annotators than summarization. we ask a crowdworker to perform perspective shift and summarization annotation for 5 hours each over different sets of dialogues. the annotator gave this unsolicited feedback: we discuss perspective shift for different dialogue subdomains briefly in appendix c. ## related work style transfer the most similar style transfer task is formalization, which has attracted attention as a standardization strategy for noisy usergenerated text. formalization can be performed as a supervised learning task, and supervised approaches often use the parallel sentence pairs from grammarly yahoo answers formality corpus @xcite . more commonly, however, formalization is performed as a semi-supervised @xcite , @xcite ) or unsupervised objective @xcite , @xcite ). another related style transfer task is the 3rd to 1st person rephrasing task proposed by @xcite . this task is evaluated with exact-match accuracy, and their best model achieves 92.8% accuracy on the test set. we conjecture perspective shift is a more difficult task because of its many-to-one nature, as well as the additional emotion attribution and formalization required. speaking-style transformation speaking-style transformation is a task which seeks to transform a literal transcription of spoken speech to one that omits disfluencies, filler words, repetitions, and other characteristics of speech that are undesirable in written text. this task attracted notice particularly in the statistical machine translation community @xcite , @xcite ). other work has focused on the opposite direction: converting written text into a style more favorable for audio listening @xcite . more recent work has focused on end-to-end modeling without this intermediate step @xcite ). this task differs from perspective shift in several respects: the focus of speaking-style transformation is on removing disfluencies, whereas perspective shift aims to preserve information that may be conveyed by the informal style of text; perspective shift requires complex coreference resolution and utterance contextualization, while speaking-style transformation leaves references unresolved; and perspective shift is applied to text post-hoc, while speaking style transcription may be performed over transcripts or in an online setting, during speech transcription. 2021 ) model the semantic structure of each speaker's contributions individually to build a global semantic structure; chen and yang (2021) extract conversational structure from several views to feed into a multi-view decoder. another approach to modeling the differences between dialogues and well-structured text is to use auxilary tasks during training @xcite . in work concurrent with this paper, @xcite propose a narrower utterance rewriting task for dialogue summarization, swapping some pronouns in the text for speaker names; however, this task does not allow for full rephrasings of the text or produce output that is in third person, making it unsuitable for extractive summarization. ## conclusion perspective shift is a new, non-trivial style transfer task that requires incorporation of coreference resolution, formalization, and emotion attribution. this paper presents a preliminary dataset for this task that includes interpretation of the meanings of conventional text abbreviations, emojis, and emoticons. the baselines presented in this paper are sufficient for downstream performance on a summarization task, but may be further improved by modeling the unique challenges of this task direction. in addition to being a challenging task, perspective shift is a useful operation for dialogue summarization. perspective shift can act as a tool for domain adaptation by shifting dialogue into a form more similar to common summarization domains (e.g. news). for extractive systems, dialogue summarization is largely infeasible because outputs will not be fluent. perspective shift allows for fluent extractive summaries. this differs from a more traditional extract-then-abstract approach because the "abstraction" (perspective shifting) step can benefit from the full document context. in a domain such as dialogue, where many utterances are strongly conditioned on the prior context of the conversation, this allows for more faithful rephrasings. when coupled with an extractive system, this perspective-shifting-based paradigm allows for the creation of more interpretable, less hallucinatory summarizations when compared to an abstractive model. other potential applications of perspective shift include direct application in abstractive summarization; in related tasks such as key point analysis @xcite , which often rely on dialogues as inputs; and for summarization of texts which contain partial dialogues, such as novels. the general strategy of transforming the input to adapt to a new domain rather than changing the model or pretraining paradigm is a promising direction because of the ease of annotation and relatively low computational cost.