Meriem Beloucif


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Probing Pre-trained Language Models for Semantic Attributes and their Values
Meriem Beloucif | Chris Biemann
Findings of the Association for Computational Linguistics: EMNLP 2021

Pretrained language models (PTLMs) yield state-of-the-art performance on many natural language processing tasks, including syntax, semantics and commonsense. In this paper, we focus on identifying to what extent do PTLMs capture semantic attributes and their values, e.g., the correlation between rich and high net worth. We use PTLMs to predict masked tokens using patterns and lists of items from Wikidata in order to verify how likely PTLMs encode semantic attributes along with their values. Such inferences based on semantics are intuitive for humans as part of our language understanding. Since PTLMs are trained on large amount of Wikipedia data we would assume that they can generate similar predictions, yet our findings reveal that PTLMs are still much worse than humans on this task. We show evidence and analysis explaining how to exploit our methodology to integrate better context and semantics into PTLMs using knowledge bases.

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Which is Better for Deep Learning: Python or MATLAB? Answering Comparative Questions in Natural Language
Viktoriia Chekalina | Alexander Bondarenko | Chris Biemann | Meriem Beloucif | Varvara Logacheva | Alexander Panchenko
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

We present a system for answering comparative questions (Is X better than Y with respect to Z?) in natural language. Answering such questions is important for assisting humans in making informed decisions. The key component of our system is a natural language interface for comparative QA that can be used in personal assistants, chatbots, and similar NLP devices. Comparative QA is a challenging NLP task, since it requires collecting support evidence from many different sources, and direct comparisons of rare objects may be not available even on the entire Web. We take the first step towards a solution for such a task offering a testbed for comparative QA in natural language by probing several methods, making the three best ones available as an online demo.


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WikiBank: Using Wikidata to Improve Multilingual Frame-Semantic Parsing
Cezar Sas | Meriem Beloucif | Anders Søgaard
Proceedings of the 12th Language Resources and Evaluation Conference

Frame-semantic annotations exist for a tiny fraction of the world’s languages, Wikidata, however, links knowledge base triples to texts in many languages, providing a common, distant supervision signal for semantic parsers. We present WikiBank, a multilingual resource of partial semantic structures that can be used to extend pre-existing resources rather than creating new man-made resources from scratch. We also integrate this form of supervision into an off-the-shelf frame-semantic parser and allow cross-lingual transfer. Using Google’s Sling architecture, we show significant improvements on the English and Spanish CoNLL 2009 datasets, whether training on the full available datasets or small subsamples thereof.


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Naive Regularizers for Low-Resource Neural Machine Translation
Meriem Beloucif | Ana Valeria Gonzalez | Marcel Bollmann | Anders Søgaard
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Neural machine translation models have little inductive bias, which can be a disadvantage in low-resource scenarios. Neural models have to be trained on large amounts of data and have been shown to perform poorly when only limited data is available. We show that using naive regularization methods, based on sentence length, punctuation and word frequencies, to penalize translations that are very different from the input sentences, consistently improves the translation quality across multiple low-resource languages. We experiment with 12 language pairs, varying the training data size between 17k to 230k sentence pairs. Our best regularizer achieves an average increase of 1.5 BLEU score and 1.0 TER score across all the language pairs. For example, we achieve a BLEU score of 26.70 on the IWSLT15 English–Vietnamese translation task simply by using relative differences in punctuation as a regularizer.


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Improving word alignment for low resource languages using English monolingual SRL
Meriem Beloucif | Markus Saers | Dekai Wu
Proceedings of the Sixth Workshop on Hybrid Approaches to Translation (HyTra6)

We introduce a new statistical machine translation approach specifically geared to learning translation from low resource languages, that exploits monolingual English semantic parsing to bias inversion transduction grammar (ITG) induction. We show that in contrast to conventional statistical machine translation (SMT) training methods, which rely heavily on phrase memorization, our approach focuses on learning bilingual correlations that help translating low resource languages, by using the output language semantic structure to further narrow down ITG constraints. This approach is motivated by previous research which has shown that injecting a semantic frame based objective function while training SMT models improves the translation quality. We show that including a monolingual semantic objective function during the learning of the translation model leads towards a semantically driven alignment which is more efficient than simply tuning loglinear mixture weights against a semantic frame based evaluation metric in the final stage of statistical machine translation training. We test our approach with three different language pairs and demonstrate that our model biases the learning towards more semantically correct alignments. Both GIZA++ and ITG based techniques fail to capture meaningful bilingual constituents, which is required when trying to learn translation models for low resource languages. In contrast, our proposed model not only improve translation by injecting a monolingual objective function to learn bilingual correlations during early training of the translation model, but also helps to learn more meaningful correlations with a relatively small data set, leading to a better alignment compared to either conventional ITG or traditional GIZA++ based approaches.

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Driving inversion transduction grammar induction with semantic evaluation
Meriem Beloucif | Dekai Wu
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics


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Improving semantic SMT via soft semantic role label constraints on ITG alignmens
Meriem Beloucif | Markus Saers | Dekai Wu
Proceedings of Machine Translation Summit XV: Papers


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Better Semantic Frame Based MT Evaluation via Inversion Transduction Grammars
Dekai Wu | Chi-kiu Lo | Meriem Beloucif | Markus Saers
Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation

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Improving MEANT based semantically tuned SMT
Meriem Beloucif | Chi-kiu Lo | Dekai Wu
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

We discuss various improvements to our MEANT tuned system, previously presented at IWSLT 2013. In our 2014 system, we incorporate this year’s improved version of MEANT, improved Chinese word segmentation, Chinese named entity recognition and dedicated proper name translation, and number expression handling. This results in a significant performance jump compared to last year’s system. We also ran preliminary experiments on tuning to IMEANT, our new ITG based variant of MEANT. The performance of tuning to IMEANT is comparable to tuning on MEANT (differences are statistically insignificant). We are presently investigating if tuning on IMEANT can produce even better results, since IMEANT was actually shown to correlate with human adequacy judgment more closely than MEANT. Finally, we ran experiments applying our new architectural improvements to a contrastive system tuned to BLEU. We observed a slightly higher jump in comparison to last year, possibly due to mismatches of MEANT’s similarity models to our new entity handling.

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XMEANT: Better semantic MT evaluation without reference translations
Chi-kiu Lo | Meriem Beloucif | Markus Saers | Dekai Wu
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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Learning to Freestyle: Hip Hop Challenge-Response Induction via Transduction Rule Segmentation
Dekai Wu | Karteek Addanki | Markus Saers | Meriem Beloucif
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Improving machine translation into Chinese by tuning against Chinese MEANT
Chi-kiu Lo | Meriem Beloucif | Dekai Wu
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

We present the first ever results showing that Chinese MT output is significantly improved by tuning a MT system against a semantic frame based objective function, MEANT, rather than an n-gram based objective function, BLEU, as measured across commonly used metrics and different test sets. Recent work showed that by preserving the meaning of the translations as captured by semantic frames in the training process, MT systems for translating into English on both formal and informal genres are constrained to produce more adequate translations by making more accurate choices on lexical output and reordering rules. In this paper we describe our experiments in IWSLT 2013 TED talk MT tasks on tuning MT systems against MEANT for translating into Chinese and English respectively. We show that the Chinese translation output benefits more from tuning a MT system against MEANT than the English translation output due to the ambiguous nature of word boundaries in Chinese. Our encouraging results show that using MEANT is a promising alternative to BLEU in both evaluating and tuning MT systems to drive the progress of MT research across different languages.