ymoslem/acl-abstracts huggingface dataset:
Detecting Annotation Errors in Morphological Data with the Transformer (2022)

Annotation errors that stem from various sources are usually unavoidable when performing large-scale annotation of linguistic data. In this paper, we evaluate the feasibility of using the Transformer model to detect various types of annotator errors in morphological data sets that contain inflected word forms. We evaluate our error detection model on four languages by introducing three different types of artificial errors in the data: (1) typographic errors, where single characters in the data are inserted, replaced, or deleted; (2) linguistic confusion errors where two inflected forms are systematically swapped; and (3) self-adversarial errors where the Transformer model itself is used to generate plausible-looking, but erroneous forms by retrieving high-scoring predictions from the search beam. Results show that the Transformer model can with perfect, or near-perfect recall detect errors in all three scenarios, even when significant amounts of the annotated data (5{\%}-30{\%}) are corrupted on all languages tested. Precision varies across the languages and types of errors, but is high enough that the model can be very effectively used to flag suspicious entries in large data sets for further scrutiny by human annotators.
Estimating the Entropy of Linguistic Distributions (2022)

Shannon entropy is often a quantity of interest to linguists studying the communicative capacity of human language. However, entropymust typically be estimated from observed data because researchers do not have access to the underlying probability distribution. While entropy estimation is a well-studied problem in other fields, there is not yet a comprehensive exploration of the efficacy of entropy estimators for use with linguistic data. In this work, we fill this void, studying the empirical effectiveness of different entropy estimators for linguistic distributions. In a replication of two recent information-theoretic linguistic studies, we find evidence that the reported effect size is over-estimated due to over-reliance on poor entropy estimators. We end this paper with a concrete recommendation for the entropy estimators that should be used in future linguistic studies.
Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Study (2022)

In recent years, a flurry of morphological datasets had emerged, most notably UniMorph, aa multi-lingual repository of inflection tables. However, the flat structure of the current morphological annotation makes the treatment of some languages quirky, if not impossible, specifically in cases of polypersonal agreement. In this paper we propose a general solution for such cases and expand the UniMorph annotation schema to naturally address this phenomenon, in which verbs agree with multiple arguments using true affixes. We apply this extended schema to one such language, Georgian, and provide a human-verified, accurate and balanced morphological dataset for Georgian verbs. The dataset has 4 times more tables and 6 times more verb forms compared to the existing UniMorph dataset, covering all possible variants of argument marking, demonstrating the adequacy of our proposed scheme. Experiments on a reinflection task show that generalization is easy when the data is split at the form level, but extremely hard when splitting along lemma lines. Expanding the other languages in UniMorph according to this schema is expected to improve both the coverage, consistency and interpretability of this benchmark.
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization (2022)

Large-scale pre-trained sequence-to-sequence models like BART and T5 achieve state-of-the-art performance on many generative NLP tasks. However, such models pose a great challenge in resource-constrained scenarios owing to their large memory requirements and high latency. To alleviate this issue, we propose to jointly distill and quantize the model, where knowledge is transferred from the full-precision teacher model to the quantized and distilled low-precision student model. Empirical analyses show that, despite the challenging nature of generative tasks, we were able to achieve a 16.5x model footprint compression ratio with little performance drop relative to the full-precision counterparts on multiple summarization and QA datasets. We further pushed the limit of compression ratio to 27.7x and presented the performance-efficiency trade-off for generative tasks using pre-trained models. To the best of our knowledge, this is the first work aiming to effectively distill and quantize sequence-to-sequence pre-trained models for language generation tasks.
Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension (2022)

Comprehending a dialogue requires a model to capture diverse kinds of key information in the utterances, which are either scattered around or implicitly implied in different turns of conversations. Therefore, dialogue comprehension requires diverse capabilities such as paraphrasing, summarizing, and commonsense reasoning. Towards the objective of pre-training a zero-shot dialogue comprehension model, we develop a novel narrative-guided pre-training strategy that learns by narrating the key information from a dialogue input. However, the dialogue-narrative parallel corpus for such a pre-training strategy is currently unavailable. For this reason, we first construct a dialogue-narrative parallel corpus by automatically aligning movie subtitles and their synopses. We then pre-train a BART model on the data and evaluate its performance on four dialogue-based tasks that require comprehension. Experimental results show that our model not only achieves superior zero-shot performance but also exhibits stronger fine-grained dialogue comprehension capabilities. The data and code are available at \url{https://github.com/zhaochaocs/Diana}.
Kronecker Decomposition for GPT Compression (2022)

GPT is an auto-regressive Transformer-based pre-trained language model which has attracted a lot of attention in the natural language processing (NLP) domain. The success of GPT is mostly attributed to its pre-training on huge amount of data and its large number of parameters. Despite the superior performance of GPT, this overparameterized nature of GPT can be very prohibitive for deploying this model on devices with limited computational power or memory. This problem can be mitigated using model compression techniques; however, compressing GPT models has not been investigated much in the literature. In this work, we use Kronecker decomposition to compress the linear mappings of the GPT-2 model. Our Kronecker GPT-2 model (KnGPT2) is initialized based on the Kronecker decomposed version of the GPT-2 model and then is undergone a very light pre- training on only a small portion of the training data with intermediate layer knowledge distillation (ILKD). Finally, our KnGPT2 is fine-tuned on downstream tasks using ILKD as well. We evaluate our model on both language modeling and General Language Understanding Evaluation benchmark tasks and show that with more efficient pre-training and similar number of parameters, our KnGPT2 outperforms the existing DistilGPT2 model significantly.
Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction (2022)

A key challenge in attribute value extraction (AVE) from e-commerce sites is how to handle a large number of attributes for diverse products. Although this challenge is partially addressed by a question answering (QA) approach which finds a value in product data for a given query (attribute), it does not work effectively for rare and ambiguous queries. We thus propose simple knowledge-driven query expansion based on possible answers (values) of a query (attribute) for QA-based AVE. We retrieve values of a query (attribute) from the training data to expand the query. We train a model with two tricks, knowledge dropout and knowledge token mixing, which mimic the imperfection of the value knowledge in testing. Experimental results on our cleaned version of AliExpress dataset show that our method improves the performance of AVE (+6.08 macro F1), especially for rare and ambiguous attributes (+7.82 and +6.86 macro F1, respectively).
Event-Event Relation Extraction using Probabilistic Box Embedding (2022)

To understand a story with multiple events, it is important to capture the proper relations across these events. However, existing event relation extraction (ERE) framework regards it as a multi-class classification task and do not guarantee any coherence between different relation types, such as anti-symmetry. If a phone line {\textquotedblleft}died{\textquotedblright} after {\textquotedblleft}storm{\textquotedblright}, then it is obvious that the {\textquotedblleft}storm{\textquotedblright} happened before the {\textquotedblleft}died{\textquotedblright}. Current framework of event relation extraction do not guarantee this coherence and thus enforces it via constraint loss function (Wang et al., 2020). In this work, we propose to modify the underlying ERE model to guarantee coherence by representing each event as a box representation (BERE) without applying explicit constraints. From our experiments, BERE also shows stronger conjunctive constraint satisfaction while performing on par or better in F1 compared to previous models with constraint injection.
Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation (2022)

End-to-end speech translation relies on data that pair source-language speech inputs with corresponding translations into a target language. Such data are notoriously scarce, making synthetic data augmentation by back-translation or knowledge distillation a necessary ingredient of end-to-end training. In this paper, we present a novel approach to data augmentation that leverages audio alignments, linguistic properties, and translation. First, we augment a transcription by sampling from a suffix memory that stores text and audio data. Second, we translate the augmented transcript. Finally, we recombine concatenated audio segments and the generated translation. Our method delivers consistent improvements of up to 0.9 and 1.1 BLEU points on top of augmentation with knowledge distillation on five language pairs on CoVoST 2 and on two language pairs on Europarl-ST, respectively.
Predicting Sentence Deletions for Text Simplification Using a Functional Discourse Structure (2022)

Document-level text simplification often deletes some sentences besides performing lexical, grammatical or structural simplification to reduce text complexity. In this work, we focus on sentence deletions for text simplification and use a news genre-specific functional discourse structure, which categorizes sentences based on their contents and their function roles in telling a news story, for predicting sentence deletion. We incorporate sentence categories into a neural net model in two ways for predicting sentence deletions, either as additional features or by jointly predicting sentence deletions and sentence categories. Experimental results using human-annotated data show that incorporating the functional structure improves the recall of sentence deletion prediction by 6.5{\%} and 10.7{\%} respectively using the two methods, and improves the overall F1-score by 3.6{\%} and 4.3{\%} respectively.
Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer (2022)

We exploit the pre-trained seq2seq model mBART for multilingual text style transfer. Using machine translated data as well as gold aligned English sentences yields state-of-the-art results in the three target languages we consider. Besides, in view of the general scarcity of parallel data, we propose a modular approach for multilingual formality transfer, which consists of two training strategies that target adaptation to both language and task. Our approach achieves competitive performance without monolingual task-specific parallel data and can be applied to other style transfer tasks as well as to other languages.
When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer Learning (2022)

Transfer learning (TL) in natural language processing (NLP) has seen a surge of interest in recent years, as pre-trained models have shown an impressive ability to transfer to novel tasks. Three main strategies have emerged for making use of multiple supervised datasets during fine-tuning: training on an intermediate task before training on the target task (STILTs), using multi-task learning (MTL) to train jointly on a supplementary task and the target task (pairwise MTL), or simply using MTL to train jointly on all available datasets (MTL-ALL). In this work, we compare all three TL methods in a comprehensive analysis on the GLUE dataset suite. We find that there is a simple heuristic for when to use one of these techniques over the other: pairwise MTL is better than STILTs when the target task has fewer instances than the supporting task and vice versa. We show that this holds true in more than 92{\%} of applicable cases on the GLUE dataset and validate this hypothesis with experiments varying dataset size. The simplicity and effectiveness of this heuristic is surprising and warrants additional exploration by the TL community. Furthermore, we find that MTL-ALL is worse than the pairwise methods in almost every case. We hope this study will aid others as they choose between TL methods for NLP tasks.
Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing (2022)

Text-to-SQL aims to parse natural language questions into SQL queries, which is valuable in providing an easy interface to access large databases. Previous work has observed that leveraging lexico-logical alignments is very helpful to improve parsing performance. However, current attention-based approaches can only model such alignments at the token level and have unsatisfactory generalization capability. In this paper, we propose a new approach to leveraging explicit lexico-logical alignments. It first identifies possible phrase-level alignments and injects them as additional contexts to guide the parsing procedure. Experimental results on $\textsc{Squall}$ show that our approach can make better use of such alignments and obtains an absolute improvement of 3.4{\%} compared with the current state-of-the-art.
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning (2022)

A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to different timestamps. TKG reasoning aims to predict potential facts in the future given the historical KG sequences. One key of this task is to mine and understand evolutional patterns of facts from these sequences. The evolutional patterns are complex in two aspects, length-diversity and time-variability. Existing models for TKG reasoning focus on modeling fact sequences of a fixed length, which cannot discover complex evolutional patterns that vary in length. Furthermore, these models are all trained offline, which cannot well adapt to the changes of evolutional patterns from then on. Thus, we propose a new model, called Complex Evolutional Network (CEN), which uses a length-aware Convolutional Neural Network (CNN) to handle evolutional patterns of different lengths via an easy-to-difficult curriculum learning strategy. Besides, we propose to learn the model under the online setting so that it can adapt to the changes of evolutional patterns over time. Extensive experiments demonstrate that CEN obtains substantial performance improvement under both the traditional offline and the proposed online settings.
Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking (2022)

Dialogue state tracking (DST) aims to extract essential information from multi-turn dialog situations and take appropriate actions. A belief state, one of the core pieces of information, refers to the subject and its specific content, and appears in the form of domain-slot-value. The trained model predicts {\textquotedblleft}accumulated{\textquotedblright} belief states in every turn, and joint goal accuracy and slot accuracy are mainly used to evaluate the prediction; however, we specify that the current evaluation metrics have a critical limitation when evaluating belief states accumulated as the dialogue proceeds, especially in the most used MultiWOZ dataset. Additionally, we propose \textbf{relative slot accuracy} to complement existing metrics. Relative slot accuracy does not depend on the number of predefined slots, and allows intuitive evaluation by assigning relative scores according to the turn of each dialog. This study also encourages not solely the reporting of joint goal accuracy, but also various complementary metrics in DST tasks for the sake of a realistic evaluation.
LM-BFF-MS: Improving Few-Shot Fine-tuning of Language Models based on Multiple Soft Demonstration Memory (2022)

LM-BFF (CITATION) achieves significant few-shot performance by using auto-generated prompts and adding demonstrations similar to an input example. To improve the approach of LM-BFF, this paper proposes \textbf{LM-BFF-MS}{---}\textbf{b}etter \textbf{f}ew-shot \textbf{f}ine-tuning of \textbf{l}anguage \textbf{m}odels with \textbf{m}ultiple \textbf{s}oft demonstrations by making its further extensions, which include 1) prompts with \textit{multiple demonstrations} based on automatic generation of multiple label words; and 2) \textit{soft demonstration memory} which consists of multiple sequences of \textit{globally shared} word embeddings for a similar context. Experiments conducted on eight NLP tasks show that LM-BFF-MS leads to improvements over LM-BFF on five tasks, particularly achieving 94.0 and 90.4 on SST-2 and MRPC, respectively.
Towards Fair Evaluation of Dialogue State Tracking by Flexible Incorporation of Turn-level Performances (2022)

Dialogue State Tracking (DST) is primarily evaluated using Joint Goal Accuracy (JGA) defined as the fraction of turns where the ground-truth dialogue state exactly matches the prediction. Generally in DST, the dialogue state or belief state for a given turn contain all the intents shown by the user till that turn. Due to this cumulative nature of the belief state, it is difficult to get a correct prediction once a misprediction has occurred. Thus, although being a useful metric, it can be harsh at times and underestimate the true potential of a DST model. Moreover, an improvement in JGA can sometimes decrease the performance of turn-level or non-cumulative belief state prediction due to inconsistency in annotations. So, using JGA as the only metric for model selection may not be ideal for all scenarios. In this work, we discuss various evaluation metrics used for DST along with their shortcomings. To address the existing issues, we propose a new evaluation metric named Flexible Goal Accuracy (FGA). FGA is a generalized version of JGA. But unlike JGA, it tries to give penalized rewards to mispredictions that are locally correct i.e. the root cause of the error is an earlier turn. By doing so, FGA considers the performance of both cumulative and turn-level prediction flexibly and provides a better insight than the existing metrics. We also show that FGA is a better discriminator of DST model performance.
Exploiting Language Model Prompts Using Similarity Measures: A Case Study on the Word-in-Context Task (2022)

As a recent development in few-shot learning, prompt-based techniques have demonstrated promising potential in a variety of natural language processing tasks. However, despite proving competitive on most tasks in the GLUE and SuperGLUE benchmarks, existing prompt-based techniques fail on the semantic distinction task of the Word-in-Context (WiC) dataset. Specifically, none of the existing few-shot approaches (including the in-context learning of GPT-3) can attain a performance that is meaningfully different from the random baseline. Trying to fill this gap, we propose a new prompting technique, based on similarity metrics, which boosts few-shot performance to the level of fully supervised methods. Our simple adaptation shows that the failure of existing prompt-based techniques in semantic distinction is due to their improper configuration, rather than lack of relevant knowledge in the representations. We also show that this approach can be effectively extended to other downstream tasks for which a single prompt is sufficient.
Hierarchical Curriculum Learning for AMR Parsing (2022)

Abstract Meaning Representation (AMR) parsing aims to translate sentences to semantic representation with a hierarchical structure, and is recently empowered by pretrained sequence-to-sequence models. However, there exists a gap between their flat training objective (i.e., equally treats all output tokens) and the hierarchical AMR structure, which limits the model generalization. To bridge this gap, we propose a Hierarchical Curriculum Learning (HCL) framework with Structure-level (SC) and Instance-level Curricula (IC). SC switches progressively from core to detail AMR semantic elements while IC transits from structure-simple to -complex AMR instances during training. Through these two warming-up processes, HCL reduces the difficulty of learning complex structures, thus the flat model can better adapt to the AMR hierarchy. Extensive experiments on AMR2.0, AMR3.0, structure-complex and out-of-distribution situations verify the effectiveness of HCL.
PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extraction (2022)

Neural models for distantly supervised relation extraction (DS-RE) encode each sentence in an entity-pair bag separately. These are then aggregated for bag-level relation prediction. Since, at encoding time, these approaches do not allow information to flow from other sentences in the bag, we believe that they do not utilize the available bag data to the fullest. In response, we explore a simple baseline approach (PARE) in which all sentences of a bag are concatenated into a passage of sentences, and encoded jointly using BERT. The contextual embeddings of tokens are aggregated using attention with the candidate relation as query {--} this summary of whole passage predicts the candidate relation. We find that our simple baseline solution outperforms existing state-of-the-art DS-RE models in both monolingual and multilingual DS-RE datasets.
To Find Waldo You Need Contextual Cues: Debiasing Who`s Waldo (2022)

We present a debiased dataset for the Person-centric Visual Grounding (PCVG) task first proposed by Cui et al. (2021) in the Who`s Waldo dataset. Given an image and a caption, PCVG requires pairing up a person`s name mentioned in a caption with a bounding box that points to the person in the image. We find that the original Who`s Waldo dataset compiled for this task contains a large number of biased samples that are solvable simply by heuristic methods; for instance, in many cases the first name in the sentence corresponds to the largest bounding box, or the sequence of names in the sentence corresponds to an exact left-to-right order in the image. Naturally, models trained on these biased data lead to over-estimation of performance on the benchmark. To enforce models being correct for the correct reasons, we design automated tools to filter and debias the original dataset by ruling out all examples of insufficient context, such as those with no verb or with a long chain of conjunct names in their captions. Our experiments show that our new sub-sampled dataset contains less bias with much lowered heuristic performances and widened gaps between heuristic and supervised methods. We also demonstrate the same benchmark model trained on our debiased training set outperforms that trained on the original biased (and larger) training set on our debiased test set. We argue our debiased dataset offers the PCVG task a more practical baseline for reliable benchmarking and future improvements.