Learning Semantic Correspondences from Noisy Data-text Pairs by Local-to-Global Alignments (2020)

Abstract: Learning semantic correspondences between structured input data (e.g., slot-value pairs) and associated texts is a core problem for many downstream NLP applications, e.g., data-to-text generation. Large-scale datasets recently proposed for generation contain loosely corresponding data text pairs, where part of spans in text cannot be aligned to its incomplete paired input. To learn semantic correspondences from such datasets, we propose a two-stage local-to-global alignment (L2GA) framework. First, a local model based on multi-instance learning is applied to build alignments for texts spans that can be directly grounded to its paired structured input. Then, a novel global model built upon a memory-guided conditional random field (CRF) layer aims to infer missing alignments for text spans which not supported by paired incomplete inputs, where the memory is designed to leverage alignment clues provided by the local model to strengthen the global model. In this way, the local model and global model can work jointly to learn semantic correspondences in the same framework. Experimental results show that our proposed method can be generalized to both restaurant and computer domains and improve the alignment accuracy.

## introduction discovering semantic correspondences between structured input (e.g., a set of slot-value pairs) and associated descriptions is a central step for many downstream nlp tasks. for example, in data-to-text generation, these correspondences indicate which subset of input data is verbalized in the description texts (what to say) and how data are described in natural language (how to say) @xcite . recent neural methods for data-to-text generation require using large-scale training corpus. these datasets are often automatically constructed from parallel structured data and descriptions in websites. some of the collected instances inevitably contain noise where input structured data are semantically inequivalent with associated texts @xcite . fig. 1 depicts an example. the slot-value pair rating:low in the input meaning representation (mr) is contradicted with the text span highly recommended in descriptions, while the text span restaurant in descriptions should refer to a slot-value pair eattype:restaurant, which is not in the paired mr. previous work @xcite @xcite ) aligns text spans in descriptions by only focusing on building semantic correspondences with the paired mr. however, in the scenario of semantically inequivalent mr-text pairs, due to the noise of input, there is not sufficient information to induce alignments for text spans such as restaurant by merely looking at its current input. to tackle the challenge of inducing correspondences (i.e., alignments) from semantically inequivalent data text pairs, we propose a local-to-global alignment (l2ga) framework. this framework is composed of a local and a global model. the local model grounds text spans in description texts with corresponding slot-value pairs in its paired mr (e.g., low price range is aligned with slot price in fig. 1 ). the global model then exploits dependencies among correspondences in the entire corpus, so that alignments for text ## task formulation here, we provide a brief description of learning alignments in semantically inequivalent data-text pairs. given a corpus with paired meaning representations (mr) and text descriptions {(r, x)} n @xmath0. the input mr @xmath1, . . . , r m ) is a set of slot-value pairs r @xmath2, v j ), where each r j contains a slot s j (e.g., price) and a value v j (e.g., cheap). the corresponding description @xmath3, . . . , x t ) is a sequence of words describing the mr. the task is to match every word x i in text x with a possible slot, where the slot can be one of m slots presented its paired mr or one of k slots in the entire corpus, where @xmath4. note that words such as stopwords in the description texts are irrelevant to any slot, we add a special label null indicating words without corresponding alignments. an example of alignments in a data-text pair is shown in fig. 1 . note that the example depicts alignments for text spans that cannot be grounded by its paired mr in two scenarios: (1) input mr contains errors where slot-value pairs are contradictory to texts (e.g., slot-value pair rating:low is contradictory to text span highly recommended), (2) extra slots in model overview golden palace is an affordable crf layer with unknown labels restaurant golden palace name less than £20 price 5 out of 5 rating input mr 𝑑𝑑 5 𝑢𝑢 5 𝑑𝑑 6 𝑢𝑢 6 𝑑𝑑 4 𝑢𝑢 4 𝑑𝑑 3 𝑢𝑢 3 𝑑𝑑 2 𝑢𝑢 2 𝑑𝑑 1 𝑢𝑢 1 local alignment model description text 𝑧𝑧 2 𝑧𝑧 3 mr encoder 𝑧𝑧 3 𝛼𝛼 𝑗𝑗 name name null null price eattype 𝑑𝑑 𝑗𝑗 = � 𝑖𝑖 𝛼𝛼 𝑖𝑖𝑗𝑗 𝑧𝑧 𝑖𝑖 descriptions (e.g., eattype:restaurant). in the next section, we present our approach to address this task. ## approach our proposed method is a local-to-global alignment (l2ga) model, as shown in fig. 2 . it is a two-stage alignment framework. the local model first encodes both description x and its paired mr r using contextualized encoders, then aligns each word in description x with a most relevant slot-value pair in mr r by computing semantic similarity based on the contextualized encodings. for semantically inequivalent data-text pairs, input mrs are not guaranteed to cover all information of corresponding descriptions. to tackle the challenge, a global model with a memory guided crf layer is proposed to exploit dependencies among alignments over the entire corpus and therefore produces missing alignments for text spans not existed in the paired mr. note that the memory is designed to integrate the alignments captured by the local model to improve the global model. ## related work previous work exploiting loosely aligned data and text corpora have mostly focused on discovering verbalisation spans for data units. these line of work usually follows a two stage paradigm, where data units are first aligned with sentences from related corpora using heuristics and then subsequently extra content is discarded in order to retain only text spans verbalising the data. @xcite use a measure of association between data units and words to obtain verbalisation spans. @xcite extract patterns from paths in dependency trees. one exception is (perez-beltrachini and lapata, 2018), the induced alignments are used to guide the generation. our work takes a step further to also induce alignments for text spans not supported by the noisy paired input with possible semantics. our work is also related to previous work on extracting information from user queries with the backend data structure. most of these approaches contain two steps. initially, a separate model is applied to match the unstructured texts with relevant input records and then an extraction model is learned based on collected annotations. @xcite and @xcite ) train a language model on data records to identify related text spans in book description. several approaches train a crf based extractor to detect the related text spans @xcite . @xcite apply a generalized expectation criteria to learn alignments between database and the texts, and train the information extractor to induce semantic annotations for text spans. compared to these work, our approach is an unified neural based alignment model which avoids the error propagation of each step. ## conclusion in this paper, we study the problem of learning alignments in semantically inequivalent data-text pairs. we propose a local-to-global framework which not only induces semantic correspondences for words that are related to its paired input but also infers potential labels for text spans that are not supported by its incomplete input. our proposed method generalizes to both restaurant and computer domains and improves the alignment accuracy. in the future, we will explore pre-trained language models @xcite for contextualized encoders to improve the alignment accuracy.