Dominican Republic

Among the most critical limitations of deep learning NLP models are their lack of interpretability, and their reliance on spurious correlations. Prior work proposed various approaches to interpreting the black-box models to unveil the spurious correlations, but the research was primarily used in human-computer interaction scenarios. It still remains underexplored whether or how such model interpretations can be used to automatically &quot;unlearn&quot; confounding features. In this work, we propose influence tuning—a procedure that leverages model interpretations to update the model parameters towards a plausible interpretation (rather than an interpretation that relies on spurious patterns in the data) in addition to learning to predict the task labels. We show that in a controlled setup, influence tuning can help deconfounding the model from spurious patterns in data, significantly outperforming baseline methods that use adversarial training.

EMNLP 2021

Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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Registered attendees will receive access the week of November 1st.

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Among the most critical limitations of deep learning NLP models are their lack of interpretability, and their reliance on spurious correlations. Prior work proposed various approaches to interpreting the black-box models to unveil the spurious correlations, but the research was primarily used in human-computer interaction scenarios. It still remains underexplored whether or how such model interpretations can be used to automatically "unlearn" confounding features. In this work, we propose influence tuning—a procedure that leverages model interpretations to update the model parameters towards a plausible interpretation (rather than an interpretation that relies on spurious patterns in the data) in addition to learning to predict the task labels. We show that in a controlled setup, influence tuning can help deconfounding the model from spurious patterns in data, significantly outperforming baseline methods that use adversarial training.

workshop paper

Intermediate task fine-tuning has been shown to culminate in large transfer gains across many NLP tasks. With an abundance of candidate datasets as well as pre-trained language models, it has become infeasible to run the cross-product of all combinations to find the best transfer setting. In this work we first establish that similar sequential fine-tuning gains can be achieved in adapter settings, and subsequently consolidate previously proposed methods that efficiently identify beneficial tasks for intermediate transfer learning. We experiment with a diverse set of 42 intermediate and 11 target English classification, multiple choice, question answering, and sequence tagging tasks. Our results show that efficient embedding based methods that rely solely on the respective datasets outperform computational expensive few-shot fine-tuning approaches. Our best methods achieve an average Regret@3 of less than 1% across all target tasks, demonstrating that we are able to efficiently identify the best datasets for intermediate training.

What to Pre-Train on? Efficient Intermediate Task Selection

Detecting events and their evolution through time is a crucial task in natural language understanding. Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs. However, embeddings in the Euclidean space cannot capture richer asymmetric relations such as event temporal relations. We thus propose to embed events into hyperbolic spaces, which are intrinsically oriented at modeling hierarchical structures. We introduce two approaches to encode events and their temporal relations in hyperbolic spaces. One approach leverages hyperbolic embeddings to directly infer event relations through simple geometrical operations. In the second one, we devise an end-to-end architecture composed of hyperbolic neural units tailored for the temporal relation extraction task. Thorough experimental assessments on widely used datasets have shown the benefits of revisiting the tasks on a different geometrical space, resulting in state-of-the-art performance on several standard metrics. Finally, the ablation study and several qualitative analyses highlighted the rich event semantics implicitly encoded into hyperbolic spaces.

Extracting Event Temporal Relations via Hyperbolic Geometry

The size of the vocabulary is a central design choice in large pretrained language models, with respect to both performance and memory requirements. Typically, subword tokenization algorithms such as byte pair encoding and WordPiece are used. In this work, we investigate the compatibility of tokenizations for multilingual static and contextualized embedding spaces and propose a measure that reflects the compatibility of tokenizations across languages. Our goal is to prevent incompatible tokenizations, e.g., "wine" (word-level) in English vs. "v i n" (character-level) in French, which make it hard to learn good multilingual semantic representations. We show that our compatibility measure allows the system designer to create vocabularies across languages that are compatible -- a desideratum that so far has been neglected in multilingual models.

Wine is not v i n. On the Compatibility of Tokenizations across Languages

Both word-level noise and sentence-level noise are challenges for Distantly Supervised Relation Extraction (DSRE). In this presentation, we introduce our novel Multi-Layer Revision Network (MLRN) which alleviates word-level noise by emphasizing inner-sentence correlations before extracting relevant information within sentences. We also devise a balanced and noise-resistant Confidence-based Multi-Instance Learning (CMIL) method which combines hard filtering and soft weighting to address sentence-level noise. Experiments show that our approach achieves significant improvements over the baselines.

Distantly Supervised Relation Extraction using Multi-Layer Revision Network and Confidence-based Multi-Instance Learning

Existing techniques for mitigating dataset bias often leverage a biased model to identify biased instances. The role of these biased instances is then reduced during the training of the main model to enhance its robustness to out-of-distribution data. A common core assumption of these techniques is that the main model handles biased instances similarly to the biased model, in that it will resort to biases whenever available. In this paper, we show that this assumption does not hold in general. We carry out a critical investigation on two well-known datasets in the domain, MNLI and FEVER, along with two biased instance detection methods, partial-input and limited-capacity models. Our experiments show that in around a third to a half of instances, the biased model is unable to predict the main model's behavior, highlighted by the significantly different parts of the input on which they base their decisions. Based on a manual validation, we also show that this estimate is highly in line with human interpretation. Our findings suggest that down-weighting of instances detected by bias detection methods, which is a widely-practiced procedure, is an unnecessary waste of training data. We release our code to facilitate reproducibility and future research.

Don't Discard All the Biased Instances: Investigating a Core Assumption in Dataset Bias Mitigation Techniques

Language use differs between domains and even within a domain, language use changes over time. For pre-trained language models like BERT, domain adaptation through continued pre-training has been shown to improve performance on in-domain downstream tasks. In this article, we investigate whether temporal adaptation can bring additional benefits. For this purpose, we introduce a corpus of social media comments sampled over three years. It contains unlabelled data for adaptation and evaluation on an upstream masked language modelling task as well as labelled data for fine-tuning and evaluation on a downstream document classification task. We find that temporality matters for both tasks: temporal adaptation improves upstream and temporal fine-tuning downstream task performance. Time-specific models generally perform better on past than on future test sets, which matches evidence on the bursty usage of topical words. However, adapting BERT to time and domain does not improve performance on the downstream task over only adapting to domain. Token-level analysis shows that temporal adaptation captures event-driven changes in language use in the downstream task, but not those changes that are actually relevant to task performance. Based on our findings, we discuss when temporal adaptation may be more effective.

Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media

Multilingual pre-trained models have demonstrated their effectiveness in many multilingual
NLP tasks and enabled zero-shot or few-shot
transfer from high-resource languages to lowresource ones. However, due to significant
typological differences and contradictions between some languages, such models usually
perform poorly on many languages and crosslingual settings, which shows the difficulty of
learning a single model to handle massive diverse languages well at the same time. To alleviate this issue, we present a new multilingual
pre-training pipeline. We propose to generate
language representation from multilingual pretrained models and conduct linguistic analysis to show that language representation similarity reflect linguistic similarity from multiple perspectives, including language family,
geographical sprachbund, lexicostatistics and
syntax. Then we cluster all the target languages into multiple groups and name each
group as a representation sprachbund. Thus,
languages in the same representation sprachbund are supposed to boost each other in both
pre-training and fine-tuning as they share rich
linguistic similarity. We pre-train one multilingual model for each representation sprachbund. Experiments are conducted on crosslingual benchmarks and significant improvements are achieved compared to strong baselines.

Discovering Representation Sprachbund For Multilingual Pre-Training

Pre-trained language-vision models have
shown remarkable performance on the visual
question answering (VQA) task. However,
most of the pre-trained models are trained
by only considering monolingual learning,
especially the resource-rich language like
English. Training such models for multilingual setups demand high computing resources and multilingual language-vision dataset
which hinders their application in practice.
To alleviate these challenges, we propose a
knowledge distillation approach to extend
an English language-vision model (teacher)
into an equally effective multilingual and
code-mixed model (student). Different from
the existing knowledge distillation methods,
which only use the output from the last layer
of the teacher network for distillation, our
student model learns and imitates the teacher
from multiple intermediate layers (language
and vision encoders) with appropriately
design distillation objectives for incremental
knowledge extraction. We also create the
large-scale multilingual and code-mixed
VQA dataset in eleven different language
setups considering the multiple Indian and
European languages. Experimental results and
in-depth analysis shows the effectiveness of
the proposed VQA model over the pre-trained
language-vision models on eleven diverse
language setups.

Towards Developing a Multilingual and Code-Mixed Visual Question Answering System by Knowledge Distillation

Asking questions about a situation is an inherent step towards understanding it. 
To this end, we introduce the task of role question generation, which, given a predicate mention and a passage, requires producing a set of questions asking about all possible semantic roles of the predicate. We develop a two-stage model for this task, which first produces a context-independent question prototype for each role and then revises it to be contextually appropriate for the passage.
Unlike most existing approaches to question generation, our approach does not require conditioning on existing answers in the text.
Instead, we condition on the type of information to inquire about, regardless of whether the answer appears explicitly in the text, could be inferred from it, or should be sought elsewhere.
Our evaluation demonstrates that we generate diverse and well-formed questions for a large, broad-coverage ontology of predicates and roles.

Asking It All: Generating Contextualized Questions for any Semantic Role

Supervised systems have nowadays become the standard recipe for Word Sense Disambiguation (WSD), with Transformer-based language models as their primary ingredient. However, while these systems have certainly attained unprecedented performances, virtually all of them operate under the constraining assumption that, given a context, each word can be disambiguated individually with no account of the other sense choices. To address this limitation and drop this assumption, we propose CONtinuous SEnse Comprehension (ConSeC), a novel approach to WSD: leveraging a recent re-framing of this task as a text extraction problem, we adapt it to our formulation and introduce a feedback loop strategy that allows the disambiguation of a target word to be conditioned not only on its context but also on the explicit senses assigned to nearby words. We evaluate ConSeC and examine how its components lead it to surpass all its competitors and set a new state of the art on English WSD. We also explore how ConSeC fares in the cross-lingual setting, focusing on 8 languages with various degrees of resource availability, and report significant improvements over prior systems. We release our code at https://github.com/sapienzanlp/consec.

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What to Pre-Train on? Efficient Intermediate Task Selection

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Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates