A Two Stage Adaptation Framework for Frame Detection via Prompt Learning (2022)

Abstract: Framing is a communication strategy to bias discussion by selecting and emphasizing. Frame detection aims to automatically analyze framing strategy. Previous works on frame detection mainly focus on a single scenario or issue, ignoring the special characteristics of frame detection that new events emerge continuously and policy agenda changes dynamically. To better deal with various context and frame typologies across different issues, we propose a two-stage adaptation framework. In the framing domain adaptation from pre-training stage, we design two tasks based on pivots and prompts to learn a transferable encoder, verbalizer, and prompts. In the downstream scenario generalization stage, the transferable components are applied to new issues and label sets. Experiment results demonstrate the effectiveness of our framework in different scenarios. Also, it shows superiority both in full-resource and low-resource conditions.

## introduction framing is a communication strategy, used to bias the discussion toward a specific stance, by selecting particular aspects of reality and making them more salient @xcite . it is widely adopted by politicians, the media, and the voting public to seek support, express opinions, and advance political agendas @xcite , thus having important implications for public opinion understanding and policy decisionmaking @xcite . @xcite propose 15 generic frame dimensions based on policy agenda, including economic, morality, and so on, paving the way for frame analysis. in order to analyze the framing strategy automatically, researchers explore the task of frame detection. given a piece of statement related to a topic, frame detection aims to recognize which dimensions of frames are employed. formally, it's a women have the freedom to make their own choices about their reproductive health care. #abortionlaw protects the safety of women. florida shooter a troubled loner with white supremacist ties. the 2nd amend does not say people need guns for self defense ! figure 1 : a brief landscape of frame detection. these three samples are taken from twitter @xcite , gvfc @xcite , fora @xcite respectively. in this paper, we propose a two-stage adaptation framework for frame detection based on prompt learning that optimizes prompts and a pre-trained language model for the general domain of framing and applies it to new scenarios. in the framing domain adaptation from pre-training stage, we train a pivot-based encoder on top of the pre-trained language model based on a generic corpus with a general framing label set. then, we design a shared prompt and several issue-specific prompts and learn their parameters by prompt learning and adversarial training to acquire transferable prompts and a verbalizer. in the downstream scenario generalization stage, we adopt the transferable components to new issues and scenarios. our contributions are mainly three-fold: ## framing domain adaptation from pre-training given a sentence x and a set of frame labels f, we aim to detect which frames are used. it is a multilabel classification problem. since we hope the model can deal with various issues and label sets, it's necessary to urge the model to learn general and transferable knowledge related to frames. in this stage, namely framing domain adaptation from pretraining, we design two tasks to achieve this goal. a generic corpus c is used as an anchor corpus for the training in this stage. ## downstream scenario generalization after the pre-training stage, we get a set of issuespecific prompt encoders, a shared prompt encoder, and an encoder that can be adapted to downstream scenarios. in the generalization stage, we reuse these transferable components for frame detection in new scenarios. as shown in figure 3 , for issues that have been "seen" in the generic corpus c, we initialize the prompt encoder by the corresponding private prompt encoder in the prompt library, and for those unseen issues, the shared prompt encoder is applied for generalization. and encoder along with its token embeddings (used for verbalizer) in the previous stage is also adopted here. ## datasets based on review of previous work on frame detection, we get datasets available mainly from three resources: (1) news media (articles), (2) social media, (3) debates and statements. five datasets are listed. we also treat each as an independent dataset, to verify effectiveness of our framework on unseen new issues like abortion. ## related work framing @xcite is a communication strategy widely adopted by news media @xcite and politicians @xcite . most researches on frames focus on news media. boydstun et al. firstly conclude 15 frames that cross-cut issues from the policy agenda and use them to analyze news articles on 3 issues. based on this, card et al. construct media frame corpus (mfc), one of the first large-scale datasets of frame annotations on news articles. this corpus is used to detect frames by dirichlet persona model @xcite , deep recurrent neural networks (naderi and hirst, 2017) and lexicon analysis @xcite . @xcite curated gun violence frame corpus (gvfc), which contains news headlines. they fine-tune bert (devlin et al., 2019) for prediction. frames have also been studied on tweets and statements of politicians and the public. johnson et al. annotated some tweets of politicians and use the probabilistic soft logic model to detect frames. roy et al. according to boydstun et al., there are mainly two frame schemas, i.e., general and issue-specific. although most work focuses on general frame prediction, some studies specialize in frames designed for a certain issue. mendelsohn et al. fine-tune roberta @xcite for analysis of different typologies of frames in immigration. liu et al. conclude 9 issue-specific frames for gun violence issue. prompt learning aims to wrap the original input text using a template with a cloze or prefix prompt, and then the language model is used to fill the unfilled information to obtain a final string, which will be mapped into labels by a verbalizer @xcite . this paradigm performs well on few-shot and zero-shot settings @xcite . prompts can be manually designed or learned with differentiable parameters @xcite . despite progress in prompt learning, few studies explore the transferability of prompts. recently, @xcite verify the effectiveness of reusing prompts of similar tasks to realize task transfer. massive work on cross-domain applies domain adversarial approaches to learn domain-invariant features @xcite . some researches on cross-domain sentiment classification extract common shared features, called pivots, which are frequent in both source and target domains and are prominent @xcite . learning pivots and non-pivots help capture features for the task, rather than a specific domain. ## conclusion and future work in this paper, we propose a general framework for frame detection that can handle various issues and frames. with the help of domain adaptation techniques, we enable both the encoder and prompts to learn transferable knowledge related to frames, thus yielding improvement on several datasets. taking advantage of prompt learning, the framework can also deal with low-resource scenarios. in the future, we plan to conduct experiments on other formulations of framing analysis, e.g., diffusion of frames.