Connecting degree and polarity: An artificial language learning study (2023)

Abstract: We investigate a new linguistic generalisation in pre-trained language models (taking BERT Devlin et al. 2019 as a case study). We focus on degree modifiers (expressions like slightly, very, rather, extremely) and test the hypothesis that the degree expressed by a modifier (low, medium or high degree) is related to the modifier’s sensitivity to sentence polarity (whether it shows preference for affirmative or negative sentences or neither). To probe this connection, we apply the Artificial Language Learning experimental paradigm from psycholinguistics to a neural language model. Our experimental results suggest that BERT generalizes in line with existing linguistic observations that relate de- gree semantics to polarity sensitivity, including the main one: low degree semantics is associated with preference towards positive polarity.

## introduction linguistic expressions can be characterized along a variety of properties: what they mean, what parts they consist of, how they combine with other expressions and so on. some of these properties are systematically related to each other. when these relations appear systematically in language after language, they can be grounds for implicational linguistic universals, for example, greenberg's universal 37: a language never has more gender categories in nonsingular numbers than in the singular. @xcite . here, two properties of linguistic expressions are related: the grammatical number of an expression and how many gender distinctions are available for this expression. more complex generalizations may concern correlation between continuous properties a and b. in order to arrive at linguistic universals connecting a and b, the relation between these properties has to be established at the level of individual languages, which is not trivial for properties with complex internal structure. in this paper, we study one such connection: the problem of polarity sensitivity of degree modifiers @xcite @xcite . degree modifiers are words like slightly, very, and extremely. property a, in this case, is the degree that these words convey, defined on a interval from very low to very high. for example, the degree of slightly is lower than the one of very. property b here encodes distributional preferences of degree modifiers with respect to polarity of a sentence where they appear -roughly, whether they appear exclusively in negative or affirmative sentences, or show no polarity preference. polarity preferences can also be represented as a continuous property from very low (negative polarity preference) to very high (positive polarity preference), with polarity-neutral in the middle. interactions between linguistic properties have been subject to experimental studies in psycholinguistics and cognitive science. one prominent method is artificial language learning @xcite @xcite @xcite . it has the following main ingredients: originally designed for studies with human participants, the artificial language learning framework has also been applied to neural network-based learning models @xcite @xcite . replacing human participants with artificial learning agents allows to examine the learning process in more detail and to make a variety of learnability statements. one important property of these experiments is that the learning agents typically come in a blank state with no prior knowledge or biases. this limits the set of linguistic questions that can be targeted by this type of experiment. the way we use the artificial language learning paradigm can be seen as middle ground between experiments with human participants and with artificial learners described above. our approach also involves an artificial language fragment and a training procedure to introduce knowledge about some property a, but it uses a pre-trained language model (lm) @xcite @xcite as the learning agent. more technically, we extend a pre-trained lm with a set of new tokens with randomly initialized embeddings and perform fine-tuning on a carefully constructed synthetic dataset. the dataset is constructed in a way to indirectly introduce different values along property a for different new tokens. upon fine-tuning, we measure how the training affected property b and how variation along b depends on the values of property a introduced during training. our study will focus on english, as represented in a pre-trained lm bert @xcite . we see this as a proof of concept work that can be extended further along the cross-linguistic dimension and in application to other models. using this set-up, we address the question of whether lms encode a connection between degree semantics and polarity, therefore making a generalization across two different linguistic properties. our approach belongs to the general area of studies using counterfactual linguistic data in nlp @xcite ; a part of that subfield that uses novel lexicon is the closest to the present paper @xcite . our work contributes to the general agenda of establishing closer connections between learning in humans and artificial neural models @xcite . the main step forward that we make with this paper is the extension of these methods to linguistic properties that have complex internal structure, rather than clear morphological or syntactic exponence. to sum up, we make the following contributions: the paper is structured as follows: section 2 gives linguistic background about degrees and polarity. section 3 describes the general method. in section 4, we define a synthetic dataset and the measures we use to estimate degree and polarity. section 5 presents the experiment. section 6 discusses our results, the limitations of our set-up and suggestions for future work. ## background: degree and polarity in this section we provide background on the studied linguistic properties: we describe degree as a property of degree modifiers, and polarity sensitivity as a property of linguistic items (words) that tend to appear in certain types of contexts. we outline the relation between these two properties, as discussed in theoretical linguistic literature. we will apply our proposed method to experimentally verify the hypothesised relation. ## method in this section, we describe the details of a method to conduct artificial language learning experiments with pretrained lms. without loss of generality, we use bert in our experiments, but other pretrained language models could be used instead. we design our method to be applied to linguistic hypotheses of the form a ⇒ b, where a, b are some properties in a given language. in this study, we specifically focus on the relationship between adverbial degree modification and polarity sensitivity. a in this context is low, medium or high degree of an adverbial modifier w, and b is negative, neutral or positive polarity of w. in general, we evaluate a hypothesis a(w, i) ⇒ b(w, j) by showing that if a holds according to bert for token w to an extent i, then so does b to some extent j, according to bert. we use the cloze test (a task where the participant is asked to recover a missing language item) adapted for bert (see @xcite ; bylinina and tikhonov 2022b for the cloze test on lms for polarity). the test uses bert's probability distributions over tokens in masked positions in diagnostic contexts for property a or b. to show that a hypothesis holds in general for an arbitatrary w, we: as part of step (1), we also verify that prior to training the initialized embeddings do not show any biases w.r.t. both properties a and b. this approach presupposes a set of contexts that distributionally select for a specific linguistic property x, denoted s(x). we obtain such contexts by mining them from data, as described in section 4.3. part of future work is to extend the current method it to a more general case. the general structure of the synthetic dataset used to fine-tune the lm is described in section 4.1. the dataset is also tailored to the linguistic phenomenon under investigation. ## dataset and measures first, we delineate a fragment of english that will be the basis of our experiment (section 4.1): simple sentences with a gradable adjective predicated over a definite noun phrase (as in the pizza is good). we re-shape these sentences to create diagnostic contexts for properties a and b (sections 4.2, 4.3). we also use it to exemplify values of a during training (section 4.3). ## experiment this section describes how we teach bert a new system of degree modifiers. section 5.1 describes how we introduced new tokens into bert's vocabulary by using particles that signal the properties we wish to teach bert. section 5.2 provides the details of the fine-tuning procedure and the experimental results. ## conclusion we introduced a method to assess the connection between two linguistic properties as encoded by pre-trained neural lms. we applied this method to an observation in linguistic semantics: the relation between degree and polarity sensitivity. we found that the experimental results are in line with the generalizations from the linguistic literature, indicating validity of our approach and pointing in the direction of bert making the generalization in question. we hope that this set-up can be applied to other types of models (trained on languages other than english, or multilingual) and other linguistic generalizations, both within individual languages and cross-linguistically.