New or Old? Exploring How Pre-Trained Language Models Represent Discourse Entities (2022)

Abstract: Recent research shows that pre-trained language models, built to generate text conditioned on some context, learn to encode syntactic knowledge to a certain degree. This has motivated researchers to move beyond the sentence-level and look into their ability to encode less studied discourse-level phenomena. In this paper, we add to the body of probing research by investigating discourse entity representations in large pre-trained language models in English. Motivated by early theories of discourse and key pieces of previous work, we focus on the information-status of entities as discourse-new or discourse-old. We present two probing models, one based on binary classification and another one on sequence labeling. The results of our experiments show that pre-trained language models do encode information on whether an entity has been introduced before or not in the discourse. However, this information alone is not sufficient to find the entities in a discourse, opening up interesting questions about the definition of entities for future work.

## introduction in a seminal paper from 1969, karttunen imagines "a device designed to read a text in some natural language, interpret it, and store the content in some manner, say, for the purpose of being able to answer questions about it". such a device-considered by him "not a practical idea, for the time being at least"-he says would need to have a particular feature, namely that it "be able to recognize when a novel individual is mentioned in the input text and to store it along with its characterization for future reference." now, more than 50 years later, neural models appear to have made such a device a practical idea after all. but do they recognize when a text introduces a new entity into the "universe of discourse", or when, in contrast, the new information concerns * shared first authorship. a previously introduced one? this is the question that we are asking in this paper. following karttunen's idea and inspired by @xcite 's analysis of information-status, we focus on discourse entities. in particular, we target the task of distinguishing between the status of entity mentions as discourse-new or discourse-old. considering that discourse entities are central to discourse theories and meaning, we consider that this is an understudied subject in the field. we take a step back from much more specific tasks such as coreference resolution and look at entities being referred to over time. we believe that the new/old distinction, as a simplified form of discourse representation, lets us ask whether language models are able to keep track of discourse entities. concretely, we build probing models that take as input the representations of pre-trained english language models and predict discourse-new/-old values for all mentions in a text. we present two probing models tackling the task on two different levels of complexity: binary classification and sequence labeling. the first probe takes one entity mention and its preceding context up to that point, and produces a binary decision (discourse-new/old). it tells us to what extent the context matters for this task. inspired by the named entity recognition task, the second probe labels each token in a sequence (new/old/outside). it tells us thus whether the entities can be localized in the sequence and labeled with the correct type. a few pieces of research have found first indications about the presence of entity knowledge in pre-trained language models. in particular, @xcite focus on identifying pronoun-antecedent pairs, leaving the question open of whether models have a general notion of entities. @xcite work only with synthetic data, which is simple in nature with short sentences and few entities. last, @xcite find that pre-trained language model's representations are unable to trace explicit entity state changes in recipes and physical processes. our findings suggest that contextualized pretrained language model representations generated with a transformer model contain enough discourse information to determine whether an entity is new or old-with results as high as 0.89 f1 in the classification probe, even beyond the case of pronouns. however, that knowledge does not suffice to localize the entity in the sequence-with results as low as 0.51 f1 in the sequence labeling task. foot_0 ## related work the intuition that language models implicitly capture and in turn also benefit from entity knowledge has been explored for some time now @xcite @xcite , with recent papers focusing on how to inject some explicit entity representation into the system @xcite . the information-status distinguishes between discourse entities that are newly introduced in the text and those that are already known to the comprehender @xcite . it is a central part in discourse theories as it accounts for the changes in referring expressions used to re-mention discourse entities as they undergo meaning updates as the context evolves. our work is most similar to that of @xcite and @xcite . both of these papers are interested in probing entity knowledge in pretrained language models at the discourse level, and they both take a semantic approach in that they are interested in the similarity between different mentions of the same entity at different points in the discourse. working with the ontonotes (pradhan et al., 2012) coreference corpus, sorodoc et al. test whether pre-trained language model representations have the morpho-syntactic and semantic knowledge required to match a pronoun with its antecedent. they report results based on pre-trained representations generated with both a transformer and an lstm model. using two baselines i) always referring to the nearest mention, ii) always referring to the most similar (cosine similarity) token, they found that a probe fed with the pre-trained embeddings succeeds at the task of predicting the correct antecedent (75.9% accuracy). an error analysis of the probe with the transformer representations showed that noun phrases were harder to solve than pronouns (so they focus on the latter). the probe also succeeded in learning agreement, as tested by inserting distractors, but accuracy drops to 53% in hard cases (e.g., when the pronoun and antecedent disagree in gender/number). in our experiments, we go a step further and consider pronouns as well as noun phrase mentions. focusing on transformers, li et al., on their side, work with the alchemy (derived from @xcite ) and textworld @xcite datasets. they use the data to construct logical propositions which are then classified into true/false with a binary classifier probe (e.g., you see an open chest. the only thing in the chest is an old key. the chest contains an apple. → true/false). this data transformation is possible because the original data are constructed short documents with simple sentences and few entities. it should be noted as well that although the probe itself is a low-capacity linear classifier, it needs a proposition embedder and a localizer of the entity in the sequence as additional pipeline components. their results are measured through accuracy (the aggregation of all propositions with an entity) and they go as high as 94%. our work is concerned with real world text data instead. as this increases the space of possible propositions, it also requires us to simplify the task, which we will explain in detail in the next sections. turning to the approach of probing or diagnostic classifiers @xcite , these are simple systems trained on the encoded representations of another system. if the probe succeeds in the task for which it is trained-discourse-new/discourseold in our case, we conclude that the input had the necessary knowledge to solve the task. research based on probing models mostly relies on classification tasks. this paper is a part of that, but here we additionally use a sequence labeling task. this strategy has precedent in examples such as ramponi et al. (2020) and dai et al. (2019a) who use sequence labeling for event and entity extraction, respectively. in the context of probing discourse knowledge, koto et al. (2021) has used this format for a sentence ranking experiment where the probe was asked to predict the most likely sentence ordering as a sequence. (1) [the researchers] t said [they] t have isolated [a plant gene that prevents [the production of [pollen] i ] j ] m . [the gene] m thus can prevent [a plant] y from fertilizing [itself] y . ## data and pre-trained model we present two sets of probing experiments on the hidden representations of a pre-trained model using real-world text data. ## probing experiments we first perform a classification task to gather information about the entity representations themselves. the second probing task looks at sequence labeling in order to evaluate whether this information can be also used to detect entity boundaries. ## discussion and analysis in line with existing literature about the presence of entity knowledge in pre-trained language models, we find that the entity knowledge extends to different types of entity mentions extracted from natural data. the high success of the binary classifier probe demonstrates that classifying an entity as new or old is not challenging, provided that the model has access to the entity representations. comparable to a coreference resolution system, this model is fed with an aggregated representation comprising all tokens in the mention, so it does not need to locate the entity in the sequence. on the other hand, finding the entities given a sequence is the hard part in our task, as shown by the sequence labeling probe. this model is superior in the heads version of the data, but less successful with spans, where the boundaries of each entity must be found. in this task, the simple pos-based baseline yields better overall results than any of our probing models. error analysis: pronouns and definite noun phrases are two types of mentions with enough regularity (i.e., closed set of forms and determiner the) for the models to exploit frequency heuristics. in this sense, comparing with the pos baseline constitutes an interesting case study ( going into details, the first thing we observe in inspecting the forms closely shows that most errors correspond to the pronouns it, this, that and which, known to be problematic for coreference resolution. for the classifier, we also found that it and that are amongst the most common errors (18/424 in spans, 23/753 in heads). we also inspected the definite noun phrases, but could not identify any specific pattern in the errors. ## conclusions and future work in this paper, we have built two probing models for the task of identifying the discourse status of entities as new or old. our models rely on binary classification and sequence labeling with input representations from a transformer-xl language model. our probes: our findings leave interesting questions for future work, in particular, defining what an entity is and what it looks like. in this sense, one could imagine a task where a probe is asked to differentiate between referring and non-referring mentions, a known and hard problem in the context of coreference resolution.