Syntactic Preposing and Discourse Relations (2024)

Abstract: Over 15 years ago, Ward & Birner (2006) suggested that non-canonical constructions in English can serve both to mark information status and to structure the information flow of discourse. One such construction is preposing, where a phrasal constituent appears to the left of its canonical position, typically sentence-initially. But computational work on discourse has, to date, ignored non-canonical syntax. We take account of non-canonical syntax by providing quantitative evidence relating NP/PP preposing to discourse relations. The evidence comes from an LLM mask-filling task that compares the predictions when a mask is inserted between the arguments of an implicit inter-sentential discourse relation — first, when the right-hand argument (Arg2) starts with a preposed constituent, and again, when that constituent is in canonical (post-verbal) position. Results show that (1) the top-ranked mask-fillers in the preposed case agree more often with “gold” annotations in the Penn Discourse TreeBank than they do in the latter case, and (2) preposing in Arg2 can affect the distribution of discourse-relational senses.

## introduction while sentences in discourse are organized in a coherent manner, there are different ways of indicating how a clause and/or sentence relates to its neighbors -with an explicit discourse connective (as in (1)) 1 , or a lexico-syntactic construction (such as the so adjective construction in (2)), or an alternative lexicalization of an explicit connective, such as provided conveying the sense as if in (3), or 1 the parts of a discourse relation are indicated by underlining explicit connectives, italicizing the first argument to the relation and bolding the second. while our focus here is on english, explicit discourse connectives have been identified in many languages including chinese, czech, french, german, lithuanian, polish, portuguese, russian and turkish @xcite . output will be gradually increased until it reaches about 11,000 barrels a day. the penn discourse treebank 3.0, abbreviated pdtb-3 @xcite , is a large manually annotated corpus of discourse relations annotated over the wall street journal section of the penn treebank (ptb) @xcite . as shown in figure 1 , while connectives could be directly extracted from the text for explicit relations, for implicit relations, human annotators were required to first insert a connective to aid in annotating the relation and then identify its sense. annotators were allowed to insert two connectives and their senses if they felt that more than one sense held between the two arguments. in their summary of work on non-canonical syntax in english, @xcite observed that linguists had identified two functions that preposed constituents serve: signalling information status and structuring information flow. we take the latter to include coherence relations between discourse units, also called discourse relations, and complement their study with quantitative work relating preposing and discourse relations. the work makes two contributions: it shows that (1) when arg2 of an inter-sentential implicit discourse relation begins with a preposed constituent, that relation is signalled more strongly than when the constituent is in canonical position, and (2) preposing in arg2 changes the distribution of discourse relational senses that hold between its arguments. in what follows, section 2 provides background motivation. sections 3 and 4 describe the data and methodology used in the experiments, with results presented in section 5. section 6 discusses the results, while the limitations section identifies limitations of the current work that should be addressed in the future. ## method this section describes (1) the process for extracting inter-sentential implicit relations in the pdtb-3 whose arg2 starts with a preposed constituent, (2) the process for creating the two datasets whose mask fillers will be compared, and (3) the maskfilling task we conduct using bert. ## analysis of predicted fillers before turning to preposing and discourse relations, we first summarize what bert chooses as mask fillers. we focus on bert's top 5 predictions because their average probabilities run from 0.41 to 0.15, 0.08, 0.05 and 0.03, resulting in their having a cumulative probability of 72%. given that the remaining probability mass is distributed across a long tail of predictions with low probability, we have not considered these predictions any further. among the top 5 predictions are (1) connectives (∼60% of the predictions in the preposed set and ∼55% in the canonical set); (2) stance adverbs such as "allegedly", "surely", "hopefully" (biber 3 ) frequency adverbs such as "often", "sometimes", "usually" @xcite ; (4) locational adverbs such as "here" and "there"; (5) temporal adverbs such as "today", "friday", "currently"; (6) focus particles such as "even", "only" and "just" @xcite ; and (7) discourse markers such as "well", "now", "anyway" @xcite . 4 the number of different lexical tokens in each category for both the preposed and the canonical set is shown in the preposed set has more discourse connectives than the canonical set, and also more focus particles, since discourse connectives are often modified by focus particles as in "even when", "only after", and "just because". in order to demonstrate the main claims of the paper (cf. section 1), we focus on the explicit connectives predicted by bert and the senses associated with them. ## results before presenting our results, we first note the evaluation measures we use in assessing bert's predictions. evaluation measures. we use two measures to evaluate the model predictions from two different perspectives. the first is accuracy, which has the value 1 (accuracy(n )=1) if any of bert's top n predictions pred n i for item i in the dataset is an explicit connective that can convey the sense annotated in the pdtb-3 (gold i ), otherwise 0. foot_3 as defined in eq. ( 1 ), we calculate and report the mean accuracy(n) for all items in a dataset. given that the top 5 predictions for each item are ordered by probability, the second measure we use is precision@n, also written p@n, which indicates the proportion of the top-n predictions that are correct. that is, p@1 indicates whether the top-1 predicted token is correct (p@1=100%) or incorrect (p@1=0%), while p@2 reports on the top-2 predictions: p@2=100% if both of the top-2 tokens are correct; p@2=50% if one of them is correct, while p@2=0% if neither is correct. similarly, for p@3, p@4 and p@5. since implicit relations in the pdtb-3 can be taken to have more than one sense, if any of the senses associated with a predicted token agree with any of the gold senses 6 , the prediction will be considered correct. a comparison between the results in to assess what bert finds hard to predict correctly when only given arg2, we count the occurrence of six most frequent one-word connectives ("and", "but", "because", "then", "so" and "with") that convey senses with a sample size of at least 95 7 in the preposed and canonical sets. (see model confidence. since there are 1052 cases where bert predicts a sense-appropriate connective in both datasets, we want to see if there is any difference in its predictions. focussing on prediction confidence, we find that in more than half of the cases (604, 57%), the top sense-appropriate predicted connective has a higher probability in the preposed set than it does in the canonical set. this suggests that bert's superior performance on the preposed set across all measures serves as empirical support for the hypothesis that preposed constituents provide evidence for the discourse relation holding between the arguments. for which senses does preposing most help bert's predictions? we examine the sense types bert correctly predicted in the preposed and canonical sets when all top 5 predictions are considered. we find that bert achieves an accuracy of ∼90% or higher for the senses expansion.conjunction, expansion.level-of-detail.arg2-as-detail, contingency.cause.reason, contingency.cause.result and comparison.concession.arg2-as-denier in the preposed set. in contrast, accuracy for the senses comparison.contrast and temporal.asynchronous.precedence is equally high (∼80%) in both sets. to determine if the performance difference is significant, we conduct chi-square tests on those sense types with a sample size of at least 95. (the significance level is chosen to be 0.05.) the results are presented in ## discussion and conclusion our study is the first to empirically validate @xcite 's claim based on qualitative linguistic evidence that preposing serves to structure information flow through a discourse. to this end, we show that preposing provides evidence for the discourse-relational sense(s) that human annotators have ascribed to the relation tokens. the evidence comes from comparing bert's performance, in terms of accuracy and confidence, across datasets used in mask-filling tasks. bert demonstrates higher accuracy and confidence when predicting connectives for implicit relations where arg2 begins with a preposed np/pp than when that np/pp appears in its canonical position. to validate the method, we feed in two variant patterns of the preposed and canonical sets that consist of only arg2. a more dramatic drop of both accuracy and p@n in the canonical set with arg2 alone sug tele-communications has a 21.8% stake, while time warner has a 17.8% stake. we also compare the distribution of sense types between the preposed set and the complement set. we provide quantitative evidence (