Dense retrieval is a basic building block of information retrieval applications. One of the main challenges of dense retrieval in real-world settings is the handling of queries containing misspelled words. A popular approach for handling misspelled queries is minimizing the representations discrepancy between misspelled queries and their pristine ones. Unlike the existing approaches, which only focus on the alignment between misspelled and pristine queries, our method also improves the contrast between each misspelled query and its surrounding queries. To assess the effectiveness of our proposed method, we compare it against the existing competitors using two benchmark datasets and two base encoders. Our method outperforms the competitors in all cases with misspelled queries. Our code and models are available at https://github.com/panuthept/DST-DenseRetrieval.

Despite their promising results on standard benchmarks, NLU models are still prone to make predictions based on shortcuts caused by unintended bias in the dataset. For example, an NLI model may use lexical overlap as a shortcut to make entailment predictions due to repetitive data generation patterns from annotators, also called annotation artifacts. In this paper, we propose a causal analysis framework to help debias NLU models. We show that (1) by defining causal relationships, we can introspect how much annotation artifacts affect the outcomes. (2) We can utilize counterfactual inference to mitigate bias with this knowledge. We found that viewing a model as a treatment can mitigate bias more effectively than viewing annotation artifacts as treatment. (3) In addition to bias mitigation, we can interpret how much each debiasing strategy is affected by annotation artifacts. Our experimental results show that using counterfactual inference can improve out-of-distribution performance in all settings while maintaining high in-distribution performance.

Cross-Lingual Retrieval Question Answering (CL-ReQA) is concerned with retrieving answer documents or passages to a question written in a different language. A common approach to CL-ReQA is to create a multilingual sentence embedding space such that question-answer pairs across different languages are close to each other. In this paper, we propose a novel CL-ReQA method utilizing the concept of language knowledge transfer and a new cross-lingual consistency training technique to create a multilingual embedding space for ReQA. To assess the effectiveness of our work, we conducted comprehensive experiments on CL-ReQA and a downstream task, machine reading QA. We compared our proposed method with the current state-of-the-art solutions across three public CL-ReQA corpora. Our method outperforms competitors in 19 out of 21 settings of CL-ReQA. When used with a downstream machine reading QA task, our method outperforms the best existing language-model-based method by 10% in F1 while being 10 times faster in sentence embedding computation. The code and models are available at https://github.com/mrpeerat/CL-ReLKT.

Sentence representations are essential in many NLP tasks operating at the sentence level.Recently, research attention has shifted towards learning how to represent sentences without any annotations, i.e., unsupervised representation learning. Despite the benefit of training without supervised data, there is still a performance penalty compared to supervised methods.Furthermore, the supervised-unsupervised performance gap widens as we reduce the model size. In this paper, we propose an unsupervised sentence representation method to reduce the supervised-unsupervised performance gap, especially for smaller models. Utilizing the concept for knowledge distillation, we derive a distillation framework comprising two training objectives, control and generalize, called ConGen. Experiments on semantic textual similarity (STS), text classification (transfer), and natural language inference (NLI) tasks show that ConGen is on par with supervised training even on smaller models.Furthermore, our method consistently outperformed competitors on multilingual STS.The code and models are available at https://github.com/KornWtp/ConGen.

Cross-lingual Sentence Retrieval (CLSR) aims at retrieving parallel sentence pairs that are translations of each other from a multilingual set of comparable documents. The retrieved parallel sentence pairs can be used in other downstream NLP tasks such as machine translation and cross-lingual word sense disambiguation. We propose a CLSR framework called Robust Fragment-level Representation (RFR) CLSR framework to address Out-of-Domain (OOD) CLSR problems. In particular, we improve the sentence retrieval robustness by representing each sentence as a collection of fragments. In this way, we change the retrieval granularity from the sentence to the fragment level. We performed CLSR experiments based on three OOD datasets, four language pairs, and three base well-known sentence encoders: m-USE, LASER, and LaBSE. Experimental results show that RFR significantly improves the base encoders’ performance for more than 85% of the cases.

Like many Natural Language Processing tasks, Thai word segmentation is domain-dependent. Researchers have been relying on transfer learning to adapt an existing model to a new domain. However, this approach is inapplicable to cases where we can interact with only input and output layers of the models, also known as “black boxes”. We propose a filter-and-refine solution based on the stacked-ensemble learning paradigm to address this black-box limitation. We conducted extensive experimental studies comparing our method against state-of-the-art models and transfer learning. Experimental results show that our proposed solution is an effective domain adaptation method and has a similar performance as the transfer learning method.