Monolingual Phrase Alignment as Parse Forest Mapping (2023)

Abstract: We tackle the problem of monolingual phrase alignment conforming to syntactic structures. The existing method formalises the problem as unordered tree mapping; hence, the alignment quality is easily affected by syntactic ambiguities. We address this problem by expanding the method to align parse forests rather than 1-best trees, where syntactic structures and phrase alignment are simultaneously identified. The proposed method achieves efficient alignment by mapping forests on a packed structure. The experimental results indicated that our method improves the phrase alignment quality of the state-of-the-art method by aligning forests rather than 1-best trees.

## introduction monolingual phrase alignment, which identifies semantically corresponding phrase pairs in sentences, is a fundamental technique useful for paraphrase recognition @xcite , textual entailment recognition @xcite , question answering @xcite , and interpreting semantic textual similarity @xcite . its ability to declare overlapping information across sentences is also useful for summarisation @xcite and for interactive document exploration @xcite . there are two approaches to phrase alignment: one aligns chunks of arbitrary spans (e.g., n-grams) @xcite @xcite while the other targets on syntactic phrases @xcite . in this study, we take the latter approach to identify phrasal paraphrases conforming to syntactic structures that allow modelling sentences based on syntax @xcite . * this work was completed at osaka university. the current state-of-the-art syntactic phrase alignment @xcite has formulated the phrase alignment as the unordered tree mapping problem between trees of source and target sentences. 1 their method realised an efficient alignment with a solid theoretical background by adopting the constrained tree edit distance algorithm @xcite , which aligns syntactic trees. however, their experiments were limited to using manually assigned gold syntactic trees, disregarding the effects of syntactic ambiguities that cause parse errors in practical parsers. we address this problem by expanding the method proposed by @xcite to align parse forests. specifically, our method considers the likelihood of both syntactic structures and phrase alignment, i.e., it simultaneously identifies the syntactic structures of input sentence pairs and phrase alignment within. figure 1 illustrates phrase alignment by our method, where the trees show syntactic structures of the source (top) and target (bottom). the alignment is colour-coded; the same colour nodes are pairs. for example, the pair of orange nodes represent that verb phrases 'made this statement' and 'gave this statement' are paraphrases. remarkably, in the source tree, the prepositional phrase 'at the karachi shipyard' (the pink node) is correctly attached to the preceding verb phrase (the orange node) because the attachment ambiguity is resolved by referring to the target. in contrast, the 1-best tree failed to derive this structure. the experimental results on the standard corpus indicated that the proposed method improves the phrase alignment quality of the state-of-the-art aligning 1-best parse trees. we also conducted a manual analysis that revealed attachment errors can be addressed by forest alignment. ## preliminary: tree alignment arase and tsujii (2020) has formulated phrase alignment as the unordered tree mapping. they adopted the constrained tree edit distance (cted) algorithm @xcite to identify optimal mappings of phrases in polynomial time. the cted algorithm is based on dynamic programming; hence their method recursively aligns phrases from leaves to root nodes of source and target syntactic trees. in the alignment algorithm, alignment of node i and j, denoted as ⟨i, j⟩, incurs a cost defined by a function γ(⟨i, j⟩) → r. in their method, the cost function is the cosine distance between phrase vectors of spans covered by i and j, where the vectors are computed by pooling token representations obtained by a fine-tuned pre-trained language model. a phrase is allowed not to have correspondence, i.e., null alignment, which is modelled as alignment to an empty node τ ϕ . the cost of null alignment is predetermined and given as a hyperparameter λ ϕ . we denote t i as the subtree rooted at node i. if we delete the node i from t i , there remain a set of subtrees whose root nodes have been the children of i: specifically, equation (1) computes the minimum cost among the cases regarding the alignment of i and j, i.e., ⟨τ ϕ , j⟩, ⟨i, τ ϕ ⟩, and ⟨i, j⟩, which correspond to the first, second, and the third expressions, respectively. notice that the last case (i.e., ⟨i, j⟩) requires the alignment cost of forests under these nodes, i.e., f s i and f t j . the cost to align f s i and f t j is computed as follows. some phrases may have long-distance correspondences @xcite that cannot be monotonically composed of alignment of descendant nodes, which hence cannot be identified by the cted algorithm. arase and tsujii (2020) align such phrases by heuristicbased post-processing. ## proposed method: forest alignment we expand the alignment method proposed by @xcite to align parse forests instead of trees. the syntactic structures of the input sentence pair are determined simultaneously with phrase alignment. a naive approach to align forests is considering combinations of all candidate trees and then finding the best one. however, this procedure is prohibitively computationally expensive considering the number of valid tree structures. we achieve efficient forest alignment by expanding the cted algorithm to perform tree mapping on a packed forest structure @xcite . ## experiment we evaluate the performance of syntactic phrase alignment of the proposed method compared to the previous state-of-the-art. ## discussion: alignment of less-similar sentences as discussed in section 1, phrase alignment is coveted by various applications like paraphrase and textual entailment recognition and question answering. such applications are different from spade, i.e., alignment of paraphrases, in that they require alignment of less-similar sentences, too. it is not a trivial difference as it sounds. as a preliminary experiment, we aligned the test set 8 of the semantic textual similarity (sts) benchmark @xcite and converted alignment costs into similarity scores. specifically, we normalised the root-level alignment costs by sentence lengths 9 and scaled them to be compatible with the sts labels, i.e., from 0 (dissimilar) to 5 (equivalent). as a result, pearson's correlation coefficient of the predicted scores and human labels was limited to 0.51, which is comparable to estimating sentence-level similarity using static word embeddings. 10 8 we excluded 7 sentence pairs that enju failed to output. 9 alignment costs obviously depend on sentence lengths. while there are only a few corpora annotating alignment on less-similar sentences @xcite , this direction is worth exploring to apply alignment techniques in practical applications. in future work, we will create corpora of this kind and explore robust phrase alignment on both similar and less-similar sentences.