This paper explores grammaticization of deverbal prepositions by a computational approach based on corpus data. Deverbal prepositions are words or phrases that are derived from a verb and that behave as a preposition such as “regarding” and “according to”. Linguistic studies have revealed important aspects of grammaticization of deverbal prepositions. This paper augments them by methods for measuring the degree of grammaticization of deverbal prepositions based on non-contextualized or contextualized word vectors. Experiments show that the methods correlate well with human judgements (as high as 0.69 in Spearman’s rank correlation coefficient). Using the best-performing method, this paper further shows that the methods support previous findings in linguistics including (i) Deverbal prepositions are marginal in terms of prepositionality; and (ii) The process where verbs are grammaticized into prepositions is gradual. As a pilot study, it also conducts a diachronic analysis of grammaticization of deverbal preposition.

Recent data-driven conversational models are able to return fluent, consistent, and informative responses to many kinds of requests and utterances in task-oriented scenarios.However, these responses are typically limited to just the immediate local topic instead of being widerranging and proactively taking the conversation further, for example making suggestions to help customers achieve their goals.This inadequacy reflects a lack of understanding of the interlocutor's situation and implicit goal.To address the problem, we introduce a task of proactive response selection based on situational information.We present a manuallycurated dataset of 1.7k English conversation examples that include situational background information plus for each conversation a set of responses, only some of which are acceptable in the situation.A responsive and informed conversation system should select the appropriate responses and avoid inappropriate ones; doing so demonstrates the ability to adequately understand the initiating request and situation.Our benchmark experiments show that this is not an easy task even for strong neural models, offering opportunities for future research.

In this paper, we propose methods 1 for discovering semantic differences in words appearing in two corpora.The key idea is to measure the coverage of meanings of a word in a corpus through the norm of its mean word vector, which is equivalent to examining a kind of variance of the word vector distribution.The proposed methods do not require alignments between words and/or corpora for comparison that previous methods do.All they require are to compute variance (or norms of mean word vectors) for each word type.Nevertheless, they rival the best-performing system in the SemEval-2020 Task 1.In addition, they are (i) robust for the skew in corpus sizes; (ii) capable of detecting semantic differences in infrequent words; and (iii) effective in pinpointing word instances that have a meaning missing in one of the two corpora under comparison.We show these advantages for historical corpora and also for native/non-native English corpora.

Naoki Otani, Michael Gamon, Sujay Kumar Jauhar, Mei Yang, Sri Raghu Malireddi, Oriana Riva. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Cross-lingual word embedding (CWE) algorithms represent words in multiple languages in a unified vector space.Multi-Word Expressions (MWE) are common in every language.When training word embeddings, each component word of an MWE gets its own separate embedding, and thus, MWEs are not translated by CWEs.We propose a simple method for word translation of MWEs to and from English in ten languages: we first compile lists of MWEs in each language and then tokenize the MWEs as single tokens before training word embeddings.CWEs are trained on a wordtranslation task using the dictionaries that only contain single words.In order to evaluate MWE translation, we created bilingual word lists from multilingual WordNet that include single-token words and MWEs, and most importantly, include MWEs that correspond to single words in another language.We show that the pre-tokenization of MWEs as single tokens performs better than averaging the embeddings of the individual tokens of the MWE.We can translate MWEs at a top-10 precision of 30-60%.The tokenization of MWEs makes the occurrences of single words in a training corpus more sparse, but we show that it does not pose negative impacts on single-word translations.

In sentiment detection, the natural language processing community has focused on determining holders, facets, and valences, but has paid little attention to the reasons for sentiment decisions. Our work considers human motives as the driver for human sentiments and addresses the problem of motive detection as the first step. Following a study in psychology, we define six basic motives that cover a wide range of topics appearing in review texts, annotate 1,600 texts in restaurant and laptop domains with the motives, and report the performance of baseline methods on this new dataset. We also show that cross-domain transfer learning boosts detection performance, which indicates that these universal motives exist across different domains.

The use of machine learning for NLP generally requires resources for training. Tasks performed in a low-resource language usually rely on labeled data in another, typically resource-rich, language. However, there might not be enough labeled data even in a resource-rich language such as English. In such cases, one approach is to use a hand-crafted approach that utilizes only a small bilingual dictionary with minimal manual verification to create distantly supervised data. Another is to explore typical machine learning techniques, for example adversarial training of bilingual word representations. We find that in event-type detection task—the task to classify [parts of] documents into a fixed set of labels—they give about the same performance. We explore ways in which the two methods can be complementary and also see how to best utilize a limited budget for manual annotation to maximize performance gain.

Considerable effort has been devoted to building commonsense knowledge bases. However, they are not available in many languages because the construction of KBs is expensive. To bridge the gap between languages, this paper addresses the problem of projecting the knowledge in English, a resource-rich language, into other languages, where the main challenge lies in projection ambiguity. This ambiguity is partially solved by machine translation and target-side knowledge base completion, but neither of them is adequately reliable by itself. We show their combination can project English commonsense knowledge into Japanese and Chinese with high precision. Our method also achieves a top-10 accuracy of 90% on the crowdsourced English–Japanese benchmark. Furthermore, we use our method to obtain 18,747 facts of accurate Japanese commonsense within a very short period.

Cross-lingual transfer of word embeddings aims to establish the semantic mappings among words in different languages by learning the transformation functions over the corresponding word embedding spaces.Successfully solving this problem would benefit many downstream tasks such as to translate text classification models from resource-rich languages (e.g.English) to low-resource languages.Supervised methods for this problem rely on the availability of cross-lingual supervision, either using parallel corpora or bilingual lexicons as the labeled data for training, which may not be available for many low resource languages.This paper proposes an unsupervised learning approach that does not require any cross-lingual labeled data.Given two monolingual word embedding spaces for any language pair, our algorithm optimizes the transformation functions in both directions simultaneously based on distributional matching as well as minimizing the backtranslation losses.We use a neural network implementation to calculate the Sinkhorn distance, a well-defined distributional similarity measure, and optimize our objective through back-propagation.Our evaluation on benchmark datasets for bilingual lexicon induction and cross-lingual word similarity prediction shows stronger or competitive performance of the proposed method compared to other stateof-the-art supervised and unsupervised baseline methods over many language pairs.

Recent work on machine translation has used crowdsourcing to reduce costs of manual evaluations.However, crowdsourced judgments are often biased and inaccurate.In this paper, we present a statistical model that aggregates many manual pairwise comparisons to robustly measure a machine translation system's performance.Our method applies graded response model from item response theory (IRT), which was originally developed for academic tests.We conducted experiments on a public dataset from the Workshop on Statistical Machine Translation 2013, and found that our approach resulted in highly interpretable estimates and was less affected by noisy judges than previously proposed methods.

Repeated labeling is a widely adopted quality control method in crowdsourcing. This method is based on selecting one reliable label from multiple labels collected by workers because a single label from only one worker has a wide variance of accuracy. Hierarchical classification, where each class has a hierarchical relationship, is a typical task in crowdsourcing. However, direct applications of existing methods designed for multi-class classification have the disadvantage of discriminating among a large number of classes. In this paper, we propose a label aggregation method for hierarchical classification tasks. Our method takes the hierarchical structure into account to handle a large number of classes and estimate worker abilities more precisely. Our method is inspired by the steps model based on item response theory, which models responses of examinees to sequentially dependent questions. We considered hierarchical classification to be a question consisting of a sequence of subquestions and built a worker response model for hierarchical classification. We conducted experiments using real crowdsourced hierarchical classification tasks and demonstrated the benefit of incorporating a hierarchical structure to improve the label aggregation accuracy.