A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations (2022)

Abstract: This paper explores a question-answer driven approach to reveal affirmative interpretations from verbal negations (i.e., when a negation cue grammatically modifies a verb). We create a new corpus consisting of 4,472 verbal negations and discover that 67.1% of them convey that an event actually occurred. Annotators generate and answer 7,277 questions % converted for 4,000 for the 3,001 negations that convey an affirmative interpretation. We first cast the problem of revealing affirmative interpretations from negations as a natural language inference (NLI) classification task. Experimental results show that state-of-the-art transformers trained with existing NLI corpora are insufficient to reveal affirmative interpretations. We also observe, however, that fine-tuning brings substantial improvements. In addition to NLI classification, we also explore the more realistic task of generating affirmative interpretations directly from negations with the T5 transformer. We conclude that the generation task remains a challenge as T5 substantially underperforms humans.

## introduction negation can be understood as an operator that transforms the meaning of some expression into another expression whose meaning is in some way opposed to the original expression @xcite . typically, negated statements are less informative than affirmative statements (e.g., "paris is not located in england" vs. "paris is located in france"). negated statements are also harder to process and understand by humans @xcite . according to @xcite , negations carry affirmative meanings. these underlying affirmative meanings, which we refer to as affirmative interpretations, range from implicatures to entailments. for example, the negated statement (1) "mary never drives long distances without a full tank of gas", carries at least the following affirmative interpretations: (1a) "mary drives long an extinct volcano is one that has not erupted in recent history. yes -what erupted? an extinct volcano -when did something erupt? in the past affirm. intp: an extinct volcano erupted in the past. it was not formed by a natural process. yes -what was formed? it -what was something formed by? an artificial process affirm. intp: it was formed by an artificial process. in order to empower models to comprehend negation, most previous works target scope @xcite and focus @xcite detection (section 2). scope refers to the part of the meaning that is negated and focus refers to the part of the scope that is most prominently or explicitly negated @xcite . scope and focus detection plays a crucial role to understand what part of a negated statement is actually negated. these tasks do not, however, reveal affirmative interpretationsthey tag tokens as belonging or not belonging to the scope and focus of a negation. in this paper, we present a question-answer driven approach to reveal affirmative interpretations from verbal negations (i.e., when a negation cue grammatically modifies a verb). we adapt qa-srl @xcite to collect questions and answers regarding the arguments of the affirmative counterpart of a negated predicate. then, we manipulate the questions and answers to generate an affirmative interpretation. we find that generating and answering questions is intuitive to non-experts (albeit they are native english speakers). consider the examples in the main contributions of this paper are: foot_0 1. a question-answer driven annotation schema to create afin, a corpus of verbal negations and their affirmative interpretations (4,472 negations, 7,277 questions and answers, and 3,001 affirmative interpretations); 2. corpus analysis indicating which predicate arguments are most often rephrased in the affirmative counterparts; 3. casting the problem of revealing affirmative interpretations as a natural language inference task and showing that it is challenging for state-of-the-art transformers; and 4. casting the problem of revealing affirmative interpretations as a generation task and showing that the t5 transformer substantially underperforms humans. ## related work revealing affirmative interpretations from negations is a challenging endeavor. in the literature, researchers primarily seek to identify scope and focus of negation. the creation of the bioscope @xcite and conandoyle-neg @xcite corpora spearheaded research on scope detection @xcite . proposals include using traditional machine learning @xcite , off-the-shelf semantic parsers and semantic representations @xcite , and neural networks @xcite . pb-foc @xcite is the largest corpus with focus of negation annotations. recent proposals for focus detection in-clude graph-based models with discourse information @xcite , neural networks with word-level and topic-level attention @xcite , and networks using scope information and context @xcite . scope and focus are useful to identify what is and what is not negated in a negated statement. consider the second example in more related to the work presented here, @xcite present a corpus of negations and their underlying affirmative interpretations (they call them positive interpretations). we are inspired by them but bypass several of their limitations. first, they only work with negations from simple wikipedia, a site devoted to english learners. as a result, their corpus uses (relatively) unsophisticated vocabulary and grammar. second, they impose several restrictions on the negations they work with (e.g., negation cue modifies root verb, sentences between 6 and 25 tokens and not including certain tokens (because, until, etc.)). third, their affirmative interpretations are restricted to a rephrasing of the statement containing negation with only one change: an argument of the negated predicate. in contrast, we barely impose restrictions on the negations we work with (no questions and no auxiliary verbs). more importantly, we introduce a questiondriven approach that allow us to obtain multiple affirmative interpretations with increasing degrees of complexity (see examples in recently, @xcite study the problem of identifying commonsense implications of negations and contradictions. more specifically, they work with if-then rules such as if x does not learn new things, then x does not gain new knowledge and if x does not leave the building, then x stays in the building. these rules capture general commonsense knowledge about what happens if an event does not occur. unlike them, we work with naturally occurring sentences that include negated predicate-argument structures with many arguments (agent, theme, manner, time, etc.). in addition, our affirmative interpretations reveal that predicates that are grammatically negated are actually factual (but with different arguments). sent. wh aux sub verb obj1 prep obj2 predicate questions (a) was something formed ? (b) does something kill someone ? (c) will someone have to do something ? argument questions (a) what was something formed by ? (b) how often does something kill someone ? (c) who will have to do something ? ## a question-answer driven approach to collect affirmative interpretations this section outlines our approach to create afin, a corpus of verbal negations and their affirmative interpretations. we first describe the sources of naturally occurring negations in our corpus. then, we outline the template-based approach to guide annotators in generating and answering questions about the affirmative counterpart of the negated predicate. lastly, we describe the process to generate natural-language affirmative interpretations from the questions and answers. ## corpus analysis the question-answer driven approach to generate and answer questions revealed that 3,001 out of the 4,472 (negated) target verbs carry an affirmative interpretation (67.1%). on average, annotators generated and answered 2.4 questions per target verb. also, the average lengths of those questions and answers (in tokens) are 5.0 and 3.5, respectively. the average negated sentence is 25. long, indicating that affirmative interpretations are much shorter than negated sentences. appendix c provides additional details, including the distribution of wh-words in the questions. percentages, shown in similar to @xcite , we manually analyze 100 random examples from our corpus to find which arguments differ in the verb-argument structure of the (negated) target verb and the affirmative counterpart. we discovered these arguments primarily have the following functions (frequencies and examples in ## experiments and discussion afin consists of sentences containing verbal negations and their affirmative interpretations in natural language. we experiment casting the problem of obtaining affirmative interpretations from negation as a natural language inference task (section 5.1) and as a generation task (section 5.2). ## conclusions we have proposed a question-answer driven approach to reveal affirmative interpretations from verbal negations. annotators generate and answer questions regarding the affirmative counterpart of a negated verb, and then we generate from them an af-firmative counterpart in natural language. through analyses, we have shown that 67.1% of verbal negations convey that the negated event is actually factual. more importantly, we observe many categories in the arguments that are replaced in the affirmative interpretations (patient, manner, quantity, time, reason, etc.). the experiments show that transformers struggle substantially when we cast the problem as nli. doing so, however, is an unrealistic scenario: affirmative interpretations are not readily available to be fed into a natural language inference classifier. further, we observe very limited success generating affirmative interpretations given as input a sentence containing verbal negation. we argue that generating affirmative interpretation is the realistic scenario and propose doing so as a challenging generation task requiring a combination of language comprehension, commonsense, and world knowledge currently out of reach for state-of-the-art models. bowei zou, guodong zhou, and qiaoming zhu. 2015. unsupervised negation focus identification with word-topic graph model. in proceedings of the 2015 conference on empirical methods in natural language processing, pages 1632-1636, lisbon, portugal. association for computational linguistics.