Taking Things Personally: Third Person to First Person Rephrasing (2021)

Abstract: The recent advancement of digital assistant technologies has opened new possibilities in the experiences they can provide. One of them is the ability to converse with a persona, e.g. celebrities, famous imaginary characters, etc. This experience requires that the replies are answered from the point of view of the persona, i.e. the first person. Since the facts about characters are typically found expressed in the third person, there is a need to rephrase them to the first person in order for the assistant not to break character and the experience to remain immersive. However, the automatic solution to such a problem is largely unexplored by the community. In this work, we present a new task for NLP: third person to first person rephrasing. We define the task and analyze its major challenges. We create and publish a novel dataset with 3493 human-annotated pairs of celebrity facts in the third person with their rephrased sentence in the first person. Moreover, we propose a transformer-based pipeline that correctly rephrases 92.0% of sentences compared to 77.0% rephrased by a rule-based baseline system.

## introduction the emergence of digital assistant technologies has opened new possibilities like creating conversational personas that impersonate celebrities or fictional characters and chat with users. when asked about themselves, these personas can respond with answers created manually by experts or taken straight from knowledge bases, which are usually stored in the third person. in order to not break character, we aim to rephrase automatically these third person replies to first person while preserving the original information. the task can be thought of as a special case of controlled text rephrasing @xcite ) and text style transfer @xcite . similarly to the above, third to first person rephrasing aims to transform a sentence while preserving the content. in our case instead of trying to change the style of the sentence, e.g. formality @xcite , sentiment @xcite , or politeness @xcite , we want to change the point of view of the sentence narrator. we define the task of third to first person rephrasing. the task's goal is to rephrase a sentence about someone in the third person point of view to the first person point of view as if that person was talking about themself. the task has a number of challenges. firstly, we can have text in quotes that should remain unchanged. secondly, reported statements should be converted to personal statements, e.g., "he said that he loves . . . " → "i love . . . ". thirdly, there could be multiple subjects in the sentences, e.g., "samuel and ron went to . . . " → "ron and i went to . . . ". finally, the point of view should be modified only on the mentions to the target persona. thus, references and co-references have to be identified and rephrased, while keeping unchanged other third person mentions that refer to another subject. this is a known problem called coreference resolution, that aims to find the same entities present in text with a number of proposed solutions in literature @xcite @xcite @xcite . for instance, the sentence about samuel l. jackson "his characters often feature the color purple: jackson chose to have doyle gipson wear a purple hat in changing lanes; mace windu, upon request by jackson to george lucas, wielded a purple lightsaber in star wars; and lazarus, his character in black snake moan, plays a purple gibson guitar." should be rephrased to "my characters often feature the color purple: i chose to have doyle gipson wear a purple hat in changing lanes; mace windu, upon my request to george lucas, i wielded a purple lightsaber in star wars; and lazarus, my character in black snake moan, plays a purple gibson guitar.". the rephraser needs to be able to disambiguate between the different subjects in complicated sentences and rephrase appropriately. the contributions of this work are threefold: the proposed pipeline establishes a benchmark for future work. ## methodology in this section, we will explain how we created our dataset for the task and present two solutions to the problem, a rule-based one and a deep learning based one. ## experiments & results the dataset (see section 2.1) was split 80% for training, 10% for evaluation and 10% for test. as the number of edits we wanted the model to do in a sentence were few, we evaluated results with a sentence accuracy metric where a rephrased sentence is correct if it perfectly matches the target sentence, and wrong otherwise. initially, we tried the rule-based system, as discussed in section 2.2, model #0. we found it got 76.2% accuracy in the test set by handling most straightforward cases, i.e., when the subject was attached to the verb and when all pronouns and possessives referred to the celebrity. also, it had problems with homographs, e.g., "her" can either be a pronoun or a possessive, so it can be replaced by "me" or "my" respectively. we investigated using the gpt-2 model to solve the task. initially we trained the model without any preprocessing and by finetuning on the nonaugmented dataset, model #1. we saw an increase in accuracy by 5.2%. the model could disambiguate between homographs without requiring any explicit input. it still had trouble though when there were multiple possible subjects. we hypothesized that by providing the model with gender and the name of the subject we wanted it to rephrase, we would see an improvement. thus, we first prepended a gender token (<@xmath0, model #2, and then, added the reference recognizer pre-processing step as described in section 2.2, model #3. compared to gpt-2 without any preprocessing step, the accuracy was raised by 6.1% and 8.6% respectively. our following hypothesis was that by using a coreferencing model, we would be able to help the model with cases where the pronouns were ambiguous, and it wasn't editing properly the text. by adding the coreference preprocessing step to the gpt-2 along with the reference recognizer and specified gender, model #4 decreased the accuracy compared to model #3. we surmised, this tiny accuracy drop was due to the limited amount of data. we did data augmentation, as discussed in section 2.2, to provide the model with more training examples of those ambiguous cases. by facing multiple similar examples with different subject names and pronouns, the model would have to take into account the subject name and the coreferences provided by the preprocessing steps. data augmentation worked, models #5 and #6, resulting in an accuracy increase compared to model #3. it is very interesting to note that the deep learning model can achieve 91.4% high accuracy without using any dependency parsing and without the coreferencing model. the best model (#6) reached 92.8% of accuracy by leveraging all the preprocessing techniques. gender-wise, model (#6) achieves 93.2% accuracy for masculine subjects and 92.0% for feminine ones. we analyzed our best model's errors (#7) and categorized them. 24% of mistakes happened due to the removal or addition of an unrelated context words (e.g., predicted "my dog" instead of "brady, my dog"). regarding reference disambiguation, other subjects were rephrased 20% of times ("i am drake's only child." instead of "he is my only model gender ref coref aug accuracy #0 76.2% #1 81.4% #2 x 87.5% #4 x x x 90.0% #3 x x 90.9% #5 x x x 91.4% #6 x x x x 92.8% ## conclusions this work presented a novel nlp task: third person to first person rephrasing. we explored the challenges of the task, when impersonating a celebrity talking about themselves. we presented a supervised corpus of celebrity facts in the first and third person. we also proposed fine-tuning gpt-2 using the dataset and explored a number of techniques that helped the model achieve 92.8% of accuracy in the test split of our dataset, a 16.6% improvement compared to the rule-based baseline. finally, we leave for future work the exploration of further refinements to our pipeline, extending our solution to other languages and augmenting the dataset.