Natural Language Decompositions of Implicit Content Enable Better Text Representations (2023)

Abstract: When people interpret text, they rely on inferences that go beyond the observed language itself. Inspired by this observation, we introduce a method for the analysis of text that takes implicitly communicated content explicitly into account. We use a large language model to produce sets of propositions that are inferentially related to the text that has been observed, then validate the plausibility of the generated content via human judgments. Incorporating these explicit representations of implicit content proves useful in multiple problem settings that involve the human interpretation of utterances: assessing the similarity of arguments, making sense of a body of opinion data, and modeling legislative behavior. Our results suggest that modeling the meanings behind observed language, rather than the literal text alone, is a valuable direction for NLP and particularly its applications to social science.

## introduction the meaning and import of an utterance are often underdetermined by the utterance itself. human interpretation involves making inferences based on the utterance to understand what it communicates @xcite . for the disciplines and applications that are concerned with making sense of large amounts of text data, human interpretation of each individual utterance is intractable. some nlp methods are designed to facilitate human interpretation of text by aggregating lexical data; for example, dictionaries map words to constructs (e.g., @xcite , and topic models discover interpretable categories in a form of automated qualitative content analysis @xcite . much of the time, though, these techniques operate over surface forms alone, limiting their ability to capture implicit content that facilitates human interpretation. while contextual embeddings are a step in this direction, these representations remain dominated by lexical content @xcite . in this work, we introduce a framework for the interpretation of text data at scale that takes implicitly communicated content more explicitly into account. specifically, we generate sets of straightforward propositions that are inferentially related to the original texts. we refer to these as inferential decompositions because they break the interpretations of utterances into smaller units. broadly speaking, we follow @xcite in distinguishing "information encoded in what is uttered" from extralinguistic information. rather than being logical entailments, these generations are plausible entailments of the kind found in discussions of textual entailment @xcite and implicature @xcite .this idea relates to decompositional semantics @xcite , but eschews linguistically-motivated annotations in favor of a more open-ended structure that facilitates direct interpretation in downstream applications. we perform this process using a large language model (llm), specifying a practitioner protocol for crafting exemplars that capture explicit and implicit propositions based on utterances sampled from the corpus of interest, thereby guiding the language model to do the same ( § 2). in designing our approach, we observe that the inherent sparseness of text data often makes it useful to represent text using lower-dimensional representations. accordingly, our notion of decomposition (and the generation process) encourages propositions that contain simple language, making them both easier to interpret and more amenable to standard embedding techniques. we observe that the viability of this technique rests on models' ability to reliably generate real-world associations @xcite @xcite , as well as their capacity to follow instructions and mirror the linguistic patterns of provided exemplars @xcite . we situate our approach within the text-as-data framework: the "systematic analysis of large-scale text collections" that can help address substantive problems within a particular discipline @xcite . first, we validate our method with human annotations, verifying that generated decompositions are plausible and easy to read, and we also show that embeddings of these inferential decompositions can be used to improve correlation with human judgments of argument similarity ( § 3). we then turn to two illustrations of the technique's utility, both drawn from real-world, substantive problems in computational social science. the first problem involves making sense of the space of public opinion, facilitating human interpretation of a set of comments responding to the us food and drug administration's plans to authorize a covid-19 vaccine for children ( § 4). the second involves the question of how likely two legislators are to vote together based on their tweets ( § 5). ## the method and its rationale the key idea in our approach is to go beyond the observable text to explicitly represent and use the kinds of implicit content that people use when interpreting text in context. consider the sentence build the wall!. following @xcite , human interpreta-human utterances communicate propositions that may or may not be explicit in the literal meaning of the utterance. for each utterance, state the implicit and explicit propositions communicated by that utterance in a brief list. implicit propositions may be inferences about the subject of the utterance or about the perspective of its author. all generated propositions should be short, independent, and written in direct speech and simple sentences. ### input: { utterance } output: { inference 1 } | { inference 2 } | . . . tion of this sentence in context involves deriving the speaker's communicative intent i from the expression itself, e, together with an implicit universe of propositions outside the utterance, u -world knowledge, hypotheses about the speaker's beliefs, and more. in this case, some elements of u might be factual background knowledge such as "the u.s. shares a border with mexico" that is not communicated by e itself. other elements might include implicitly communicated propositions such as "immigration should be limited". propositions from this latter category, consisting of relevant plausible entailments from the utterance, we denote as r ⊂ u . 2 we are motivated by the idea that, if human interpretation includes the identification of plausible entailments r based on the expressed e, automated text analysis can also benefit from such inferences, particularly in scenarios where understanding text goes beyond "who did what to whom". the core of our approach is to take an expression and explicitly represent, as language, a body of propositions that are related inferentially to it. operationally, our method is as follows: definitely probably may or may not probably not definitely not 0% 20% 40% 60% plausibility examples paraphrase explicit implicit to ground these propositions in a context, we develop domain-dependent user guidelines (step 2) for exemplar creation, limiting the focus to inferences about the utterance topic and its speaker (and explicit content, appendix a.2). 4 continuing with the example e of build the wall!, the method might generate the augmented representation @xmath0, illegal immigation should be stopped, . . .} as plausible inferences about the speaker's perspective (an actual example appears in fig. 1 ). because the propositions are expressed in simple language, they are easier to represent with standard embedding techniques: when using k-means clustering to over the embedded representations, the clusters are far more readable and distinct than baselines ( § 4). hence, this approach facilitates interpretation of text data at scale. 5 ## generation validity text analysis methods used in the computational social sciences have known issues with validity. the interpretation of unsupervised models is particularly fraught-leading to potentially incorrect inferences about the data under study @xcite . we validate the two components of our approach: the quality of the generations and the similarity of their embeddings. first, if we want to use the generated inferential decompositions in downstream applications, it is important that they are reasonable inferences from the original utterance. large language models are known to hallucinate incorrect information "scale" refers to dataset sizes that render human analysis too costly, and is constrained only by computational budget. in some circumstances @xcite @xcite , but can also exhibit high factuality relative to prior methods @xcite . this leads to the question: do language models reliably produce plausible explicit & implicit propositions? second, since similarity over embedded text underpins both the text analysis ( § 4) and downstream application ( § 5) that validate our approach, we measure the correlation of embedding and groundtruth similarities for several standard semantictextual-similarity tasks. assuming that human similarity judgments make use of both explicit and implicit inferences, we also ask whether including such information in sentence representations improve automated estimates of similarity. ## inferential decompositions help theme discovery since the augmented representations we are creating go beyond the observed text to inferentially related propositions, we expect it to be useful in problem settings where observable text is the "tip of the iceberg" -intuitively, problems where it is particularly important to consider not only what was said, but what is behind what was said. specifically, 8 for example, from a 38-word utterance, explicit: "the minimum wage should be higher than $7.25"; implicit: "the current minimum wage is insufficient" 9 even the "explicit" setting generates "chris kelly has died" from the original "rip to tha mac daddy chris kelly". 10 altering the prompt to support sts by instead generating paraphrases leads to state-of-the-art results, appendix a.3. clusters of decompositions (our method) take significantly less time to review and are more distinctive from one another. relatedness scores are high for the observed comments, but significantly worse membership identification scores reveal this to be a spurious result owed to the topical homogeneity of the dataset (all comments are about covid vaccines). all differences are significant at @xmath1.05 except membership scores between comments and sentences and evaluation times for sentences and decompositions. understanding public opinion on a contentious issue fits that description: expressions of opinion are generated from a more complex interplay of personal values and background beliefs about the world. this is a substantive real-world problem; in the us, federal agencies are required to solicit and review public comments to inform policy. our approach is related to efforts showing that intermediate text representations are useful for interpretive work in the computational social sciences and digital humanities, where they can be aggregated to help uncover high-level themes and narratives in text collections @xcite . in a similar vein, we cluster inferential decompositions of utterances that express opinion to uncover latent structure analogous to the discovery of narratives in prior work. we analyze a corpus of public comments to the u.s food and drug administration (fda) concerning the emergency authorization of covid-19 vaccinations in children -in terms of content and goals, our application resembles the latent argument extraction of @xcite , who, building on content analysis by @xcite , clustered tweets relating to covid-19 to facilitate effective annotation by a group of human experts. in our case, we not only discover valuable latent categories, but we are able to assign naturalistic category labels automatically in an unsupervised way. 11 dataset. we randomly sampled 10k responses from a set of about 130k responses to a request for comments by the fda regarding child vaccine authorization. 12 our dataset contains often-lengthy comments expressing overlapping opinions, colloquial language, false beliefs or assumptions about the content or efficacy of the vaccine, and a general attitude of vaccine hesitancy (see the "comment" column of method. we generate 27,848 unique inferential decompositions from the observable comments at an average of 2.7 per comment. we use k-means clustering to identify categories of opinion, varying k. specifically, two authors created 31 exemplars from seven original comments from the dataset that exhibit a mixture of implicit proposition types ( automated evaluation. we lack ground truth labels for which documents belong to which cluster, so we first turn to intrinsic metrics of cluster evaluation: the silhouette (rousseeuw, 1987), source cluster 1 cluster 2 cluster 3 decomposition clusters the vaccine may be harmful to children. children are vulnerable to the long-term effects of the vaccine. vaccines are still under trials and their side effects are unknown. the vaccine has not been properly tested. there is not enough data on the vaccine's long-term effects. natural immunity is better than vaccineinduced immunity. natural immunity is important for children to develop. natural immunity is more effective. natural immunity is better for fighting the covid virus. people should develop immunities to stay healthy. natural immunity should be reflected in science. the government is attempting to control citizens. american rights are being eroded. the government should investigate the use of we need to protect our children. the us values freedom and choice. expert narratives wawrzuta et al. (2021) the vaccine is not properly tested, it was developed too quickly natural methods of protection are better than the vaccine lack of trust in the government crowdworker label long-term vaccine worries natural immunity control by government calinski-harabasz @xcite , and davies-bouldin @xcite scores; roughly speaking, these variously measure the compactness and distinctiveness of clusters. since metrics can be sensitive to the quantity of data in a corpus (even if operating over the same content), we subsample the sentence and decomposition sets to have the same size as the comments (10k). 13 clusters of decompositions dramatically outperform clusters of comments and sentences across all metrics for each cluster size-in fact, independent of cluster size, the best scores are obtained by decomposition clusters ( ## decompositions support analyses of legislator behavior having established our method's ability to facilitate human interpretation of text data ( § 3 and § 4), we now examine the usefulness of generated inferential decompositions in a very different downstream application where the relevant analysis of text is likely to involve more than just overt language content. here, we model legislator behavior using their speech (here, tweets), asking: does the similarity between legislators' propositions help explain the similarity of their voting behavior? traditional theories of homophily @xcite suggest that shared properties (e.g., electoral geography, clark and caro, 2013) increase the likelihood that two legislators vote the same way -and many research questions have centered around such co-voting @xcite @xcite . in preliminary experiments we found when modeling co-voting, text similarity between legislator was a valuable predictor of co-voting @xcite . here we posit that such modeling can further benefit by going beyond surface text to capture inferentially related propositions related to viewpoints. consider figure 1 . while the observable tweets themselves are not similar in their surface form, they express similar views regarding the importance of the preservation of nature, something that would be clear to a human reader interpreting them. this can be captured by inferential decompositions that reveal authors' viewpoints toward the issue. we operationalize our method for this purpose by creating exemplars that contain inferences about the utterance topic and perspective ( model setup. for the task of modeling co-vote behavior, we extend the framework introduced by @xcite to incorporate individuals' language into the model. at a high level, we operationalize legislator homophily by measuring the similarities of their embedded speech twitter. following @xcite and @xcite , we model the log odds ratio of the co-voting rate be-tween a pair of legislators i, j using a mixed effects regression model, controlling for the random effects of both actors under consideration. the covote rate λ is the number of times the legislators vote the same way -yea or nay -divided by their total votes in common within a legislative session. dataset. the goal is to represent each legislator using their language in such a way that we can measure their similarity to other legislators-our informal hypothesis of the data-generating process is that a latent ideology drives both vote and speech behavior. specifically, we follow vafa et al. (2020) by using their tweets; data span the 115 th -117 th sessions of the us senate (2017-2021). 14 we further suppose that ideological differences are most evident when conditioned on a particular issue, such as "the environment"; in fact, @xcite note that aggregated measures like ideal points mask important variation across issues. to this end, we first train a topic model on twitter data to group legislator utterances into broad issues. 15 two authors independently labeled the topics they deemed most indicative of ideology, based on the top words and documents from the topic-word (β (k) ) and topicdocument (θ (k) ) distributions. 16 then, for each selected topic k and legislator l, we select the top five tweets u ## related work as a computational text analysis method that operates over reduced representations of text data, our work is similar to that of @xcite and @xcite . closest to our clustering effort ( § 4), @xcite extract and cluster paraphrastic propositions to generate summaries. however, they constrain themselves to the observed content alone, limiting their ability to capture context-dependent meaning. our method continues the relaxation of formal semantic representations in nlp, a process exemplified by the decompositional semantics initiative @xcite , which aims to create an intuitive semantic annotation framework robust to variations in naturalistic text data. the generated inferential decompositions in our work are human-readable and open-ended, structured by a user-defined schema in the form of exemplars. in this way, we also follow a path forged by natural language inference @xcite , which has increasingly relaxed semantic formalisms when creating datasets. in future work, we plan to relate both the exemplars and outputs to formal categories and investigate their utility in downstream tasks. our methods for generating decompositions are distinct from extracting commonsense knowledge from text-e.g., through templates @xcite , free-form generation from llms @xcite , or script induction @xcite (which can also use llms, sancheti and rudinger 2022). our decompositions are not designed to produce general commonsense knowledge such as "rain makes roads slippery" @xcite , but instead to surface the explicit or implicit propositions inferrable from an utterance. our work also bears a similarity to @xcite , who increase the interpretability of sentence embeddings using an amr graph for a sentence. however, amr graphs are also tied to the information present in an utterance's surface form (and, moreover, it is unclear whether amr parsers can accommodate noisier, naturalistic text). llms have been used to generate new information ad-hoc in other settings, for example by augmenting queries @xcite or creating additional subquestions in question-answering @xcite . in contemporaneous work, @xcite use an llm to generate abstract descriptions of text to facilitate retrieval. in @xcite , the authors model writer intent from a headline with llms. @xcite generate implicit knowledge that conceptually connects contiguous sentences in a longer body of text. ## conclusion our method of inferential decompositions is useful for text-as-data applications. first, we uncover high-level narratives in public commentary, which are often not expressed in surface forms. second, we show that, by considering alternative representations of legislators' speech, we can better explain their joint voting behavior. more broadly, treating implicit content as a first-class citizen in nlp-a capability enabled via generation in large language models-has the potential to transform the way we approach problems that depend on understanding what is behind people's utterances, rather than just the content of the utterances themselves. our validity checks in section 3 reveal that while most decompositions are deemed reasonable by humans, some are not (fig. 3 .). it remains to be studied the extent to which implausible generations are affecting with the results, or if they are introducing harmful propositions not present in the original text. in future work, we will explore whether known political biases of language models @xcite affect our results. although an open-source model (specifically alpaca- @xcite produces similar results to those reported, our main experiments primarily use models released by ope-nai, which may lead to potential reproducibility issues. all our analyses and experiments focus on utterances in the english language, which could limit the generalizability of our method. relatedly, our experiments are also specific to the us sociocultural context and rely on models that are known to be western-centric @xcite . the embeddings could be made more sensitive to the particular use case. in future work, we plan to additionally fine-tune the embeddings so that they are more sensitive to the particular use case (e.g., establishing argument similarity, behrendt and harmeling, 2021)).