Auto-Encoding Scene Graphs for Image Captioning

We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions. Intuitively, we humans use the inductive bias to compose collocations and contextual inference in discourse. For example, when we see the relation `person on bike', it is natural to replace `on' with `ride' and infer `person riding bike on a road' even the `road' is not evident. Therefore, exploiting such bias as a language prior is expected to help the conventional encoder-decoder models less likely overfit to the dataset bias and focus on reasoning. Specifically, we use the scene graph --- a directed graph ($\mathcal{G}$) where an object node is connected by adjective nodes and relationship nodes --- to represent the complex structural layout of both image ($\mathcal{I}$) and sentence ($\mathcal{S}$). In the textual domain, we use SGAE to learn a dictionary ($\mathcal{D}$) that helps to reconstruct sentences in the $\mathcal{S}\rightarrow \mathcal{G} \rightarrow \mathcal{D} \rightarrow \mathcal{S}$ pipeline, where $\mathcal{D}$ encodes the desired language prior; in the vision-language domain, we use the shared $\mathcal{D}$ to guide the encoder-decoder in the $\mathcal{I}\rightarrow \mathcal{G}\rightarrow \mathcal{D} \rightarrow \mathcal{S}$ pipeline. Thanks to the scene graph representation and shared dictionary, the inductive bias is transferred across domains in principle. We validate the effectiveness of SGAE on the challenging MS-COCO image captioning benchmark, e.g., our SGAE-based single-model achieves a new state-of-the-art $127.8$ CIDEr-D on the Karpathy split, and a competitive $125.5$ CIDEr-D (c40) on the official server even compared to other ensemble models

Attention control comparisons with SLT for people with aphasia following stroke: methodological concerns raised following a systematic review

Objective: Attention control comparisons in trials of stroke rehabilitation require care to minimize the risk of comparison choice bias. We compared the similarities and differences in SLT and social support control interventions for people with aphasia. Data sources: Trial data from the 2016 Cochrane systematic review of SLT for aphasia after stroke. Methods: Direct and indirect comparisons between SLT, social support and no therapy controls. We double-data extracted intervention details using the template for intervention description and replication. Standardized mean differences and risk ratios (95% confidence intervals (CIs)) were calculated. Results: Seven trials compared SLT with social support (n  =  447). Interventions were matched in format, frequency, intensity, duration and dose. Procedures and materials were often shared across interventions. Social support providers received specialist training and support. Targeted language rehabilitation was only described in therapy interventions. Higher drop-out (P  =  0.005, odds ratio (OR) 0.51, 95% CI 0.32–0.81) and non-adherence to social support interventions (P  <  0.00001, OR 0.18, 95% CI 0.09–0.37) indicated an imbalance in completion rates increasing the risk of control comparison bias. Conclusion: Distinctions between social support and therapy interventions were eroded. Theoretically based language rehabilitation was the remaining difference in therapy interventions. Social support is an important adjunct to formal language rehabilitation. Therapists should continue to enable those close to the person with aphasia to provide tailored communication support, functional language stimulation and opportunities to apply rehabilitation gains. Systematic group differences in completion rates is a design-related risk of bias in outcomes observed

Using Achievement Tests to Measure Language Assimilation and Language Bias among Immigrant Children

We use Woodcock Johnson III child assessment data in the New Immigrant Survey to examine language assimilation and test score bias among children of Hispanic immigrants. Our identification strategy exploits the test language randomization (Spanish or English) to quantitatively measure the degree and speed of language assimilation, in addition to the potential costs associated with taking a test in one’s non-dominant language. We find that U.S. born children of Hispanic immigrants are not bilingual as predicted by most language assimilation models but rather are English dominant. English language assimilation occurs at a rapid pace for foreign born children as well; children who arrive in the U.S. at an early age or who have spent more than four years in the U.S. do not benefit from taking the tests in Spanish. Results are robust to a fixed effects specification that controls for household level characteristics constant across siblings.immigration, language assimilation, New Immigrant Survey, Woodcock Johnson achievement tests

Language (Technology) is Power: A Critical Survey of "Bias" in NLP

We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process. We further find that these papers' proposed quantitative techniques for measuring or mitigating "bias" are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing "bias" in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of "bias"---i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements---and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities

The Language of Bias: A Linguistic Approach to Understanding Intergroup Relations

[Excerpt] This chapter explores the role of language in the relationship between diversity and team performance. Specifically, we consider how a linguistic approach to social categorization may be used to study the social psychological mechanisms that underlie diversity effects. Using the results of a study examining the effects of gender, ethnicity and tenure on language abstraction, we consider the potential implications for team processes and effectiveness. In addition, we propose a revised team input-process-output model that highlights the potential effects of language on team processes. We conclude by suggesting directions for future research linking diversity, linguistic categorization and team effectiveness